Sherbert. Serving up the daily AI scoop.
Grok Bot now places phone calls on your behalf with live transcripts, request handling, and conversation summaries. Version 0.77 adds Tailscale private networking, Vault for credentials and payments, Mac Messages setup, and sharing controls while Grok models 4.5 through 4.7 became native first-party options in the Cursor model picker with full agent tool support. These releases embed Grok more deeply into daily coding, communication, and infrastructure tasks for X subscribers.
The facts up top. Quotes, chatter, and the full PTR stay folded.
Copy-trade Cool Pelosi/politician copy-trade quiet on X in last 30hrs
| Name | Last | 1d | 5d | 21d |
|---|---|---|---|---|
| NVDA Nvidia | 210.96 | -0.59% | -1.48% | -6.06% |
| AMD AMD | 516.79 | +1.74% | -2.26% | -17.87% |
| AVGO Broadcom | 321.68 | -0.48% | -2.15% | -8.19% |
| TSM TSMC | 418.51 | +1.49% | +1.43% | -7.23% |
| ASML ASML | 1,571.80 | +0.72% | -4.25% | -8.75% |
| SMCI Super Micro | 32.94 | -2.31% | +4.14% | -20.65% |
| INTC Intel | 86.96 | +1.72% | -3.22% | -31.74% |
| MU Micron | 877.55 | +2.46% | -0.15% | -18.77% |
| ARM Arm | 210.33 | +0.11% | -7.67% | -31.34% |
| CRWV CoreWeave | 54.24 | -2.46% | -11.65% | -39.83% |
| MSFT Microsoft | 524.51 | +0.66% | -0.86% | +5.34% |
| GOOGL Alphabet | 320.21 | -1.24% | -5.98% | -6.47% |
| AMZN Amazon | 248.00 | -0.95% | -0.80% | -0.55% |
| META Meta | 642.62 | -0.96% | -9.40% | -17.36% |
| AAPL Apple | 347.73 | -0.14% | +1.24% | +3.52% |
| TSLA Tesla | 372.22 | -0.63% | +4.93% | -1.51% |
| ORCL Oracle | 126.16 | +0.49% | -1.61% | -9.25% |
| PLTR Palantir | 196.75 | +0.29% | +5.76% | +2.16% |
Source: delayed Yahoo Finance, Stooq if Yahoo blanks. Do not treat this as a ticket.
The house files late. People still copy it. That bid can lift the names they still hold. Maybe a bubble. Short game: ride the tide, sell when you are up.
Also on the filing, not a tape name.
Parsed from the official House PDF when a new doc_id hits the Clerk index. Amounts are ranges. Many rows are spouse (SP). Delayed quotes. Not a ticket.
What I clipped from today's AI news.
Grok 4.5, 4.6, and 4.7 are now first-party choices in Cursor, with full access to the coding agent's tools. It gives Cursor users another frontier-model option without wiring up a separate provider.
Grok Bot can now place phone calls to people or businesses, make requests, capture a transcript, and send back a summary. It is a concrete move from chat-based assistance toward agents that act outside the browser.
Grok Bot 0.77 adds Tailscale access for approved private networks, a Vault-backed payments screen, Mac Messages setup, and sharing. Sharing requires Premium or Ultra, while the update expands the bot's reach into personal systems and transactions.
Grok Bot 0.75.5 added Vault for stored logins, cards, and contacts, plus email forwarding that preserves attachments and formatting. It also tracks who asked for Slack messages, a small but useful step for auditing agent actions.
Grok Build 1.0.70 adds clearer spacing between answers and makes its /plugins command report only loaded skills, including any it skipped. It is a small usability update for people working with the coding tool's plugin system.
SpaceX has reportedly closed $40 billion in callable debt to buy Nvidia GPUs scheduled for delivery in early 2027. It is another sign that AI compute is becoming a capital-intensive infrastructure race, with smaller cloud GPU providers facing a tougher market.
Anthropic has confidentially filed an S-1 with the SEC, the first formal step toward a possible IPO. The filing puts fresh attention on the central AI business tension: fast-growing revenue alongside enormous compute costs and losses.
AI-linked stocks finished effectively flat, with nine names up and nine down in the tracked basket. Micron led the group, CoreWeave lagged, and Nvidia trailed the Nasdaq-100 over the past 21 sessions.
CoreWeave fell 2.46% in the latest delayed quote and is down 11.65% over five sessions. The move is a reminder that public AI infrastructure companies remain sensitive to spending expectations and financing pressure.
Trade trackers flagged Pelosi household purchases in Bloom Energy and Intel, both tied to the power and chip buildout behind AI data centers. The disclosures arrived roughly three to four weeks after the trades, so copy-trade feeds are following delayed information, not live signals.
A new Pelosi household transaction filing listed no public AI or tech ticker purchases. The broader lesson is that congressional trade disclosures use value ranges and arrive late, which makes copy-trading narratives far less precise than they look.
TSMC is asking suppliers for chip-factory automation that uses fewer rare-earth magnets, reducing exposure to China’s export licensing rules. It is a reminder that AI chip supply depends on far more than GPUs.
Elon Musk says SpaceX closed $40 billion in debt financing for GPUs due in the first half of 2027. The number underlines how much borrowed capital is now flowing into AI infrastructure.
ByteDance, Alibaba, and Tencent reportedly ordered more than $35 billion of Nvidia’s China-compliant RTX PRO 5500 GPUs. Nvidia may only be able to supply 5% to 10% of the requested volume, keeping AI compute scarce even for its biggest buyers.
OpenAI is reportedly offering steep enterprise discounts, in some cases as high as 90%, to win Anthropic customers. The AI platform fight is becoming a price war, not just a benchmark war. ChatGPT Enterprise is paid.
Reports flagged two separate AI security issues: Claude allegedly accessed voter data behind a paywall, and OpenAI identified a critical flaw in Brotli, a widely used compression library. AI systems increasingly inherit the security and access-control weaknesses of the web and software around them.
Musk says SpaceX raised $40 billion in callable debt largely for GPU purchases expected in early 2027. He also predicts weaker AI cloud providers will struggle as lenders demand real revenue behind data-center debt.
Meta shares fell 9.4% over five sessions, based on delayed market data. The move puts fresh attention on how investors are pricing the company’s enormous AI spending and its path to returns.
Swedish startup Strawberry has launched an enterprise AI agent platform ahead of a planned Series A round. It joins the growing field of companies trying to package autonomous task-running systems for business deployment.
Former Google CEO Eric Schmidt and co-authors argue that OpenAI and Anthropic resemble chartered trading companies: privately run institutions with outsized public power. It is a sharp framing of the governance problem around frontier AI labs.
Alpha Dhabi committed $1 billion to the Alpha Wave III AI fund, adding more institutional money to the race for AI stakes. The fund has already invested across names including SpaceX, OpenAI, and Anthropic.
A new study finds frontier language models confidently misidentify the age and gender of people from short handwritten responses, then fail to correct themselves when challenged. It is another warning that polished model explanations do not guarantee sound reasoning.
Nvidia open-sourced Flora, a system for labeling robot video that it says cuts the cost of an hour of footage from $88 to 72 cents while slightly improving quality. Cheaper training data could speed up robotics development. Flora is free to download.
No posts or replies from @pelositracker, @joinautopilot or mentions across Unusual Whales, TrackingItNow, Capitol Trades since 2026-10-25. Searches returned zero relevant activity. Latest filings weeks old; trackers not posting. Retail social momentum faded.
Researchers propose a privacy-preserving video-monitoring system that explains unusual events and points to their location without relying on identifiable people. Better explanations could make anomaly-detection systems easier to audit and deploy.
247 points, 140 comments
Try Browserbase, the Browser Agent Platform!: https://browserbase.com 🚨 HUGE AI NEWS! OpenAI's rumored GPT-7 Bel is making headlines as new details emerge about its frontier model training, security upgrades, and a potential GPT-6.1 Astra release! Meanwhile, Anthropic is facing backlash over Claude's reverse-engineering restrictions, Google's Gemini 4 Carbon is looking INSANE, and major Qwen 4 leaks are revealing some impressive new capabilities! 🚨 Everything you’re about to see, I benchmarked
88 points, 111 comments
arXiv:2610.31838v1 Announce Type: new Abstract: This paper introduces Sense-Aligned Grounding Evaluation (SAGE), a unified 10-candidate image-selection benchmark that reformulates VWSD, VerSe, and WikiDiverse into a shared term-centric protocol covering noun-, verb-, and entity-centered grounding. SAGE includes confusable hard negatives and a context-swapped VerSe diagnostic to probe target-term grounding beyond direct caption--image compatibility. We further propose Gloss-Augmented Reasoning (GAR), a training-free framework combining definition grounding with constraint-guided semantic verifi
arXiv:2610.31945v1 Announce Type: new Abstract: This report introduces Kolibri, an English-German mixture-of-experts transformer with 78.1B total parameters and 3.46B active parameters per token, released as open weights under the Apache 2.0 licence. We built Kolibri for sovereign and specialised deployment, with a particular focus on German and on regulated domains such as public administration, industry, and aerospace. To support these settings, we control the full model-development process, including data, architecture, training infrastructure, post-training, and evaluation. We train Kolibr
556 points, 237 comments
This paper proposes an offline test harness for enterprise agents whose live data changes over time. It aims to make prompt-compression experiments reproducible, so teams can measure cost savings without testing against stale or drifting backend state.
Tsinghua released AKV, a server for LLM agents that manages their KV cache, the model’s stored conversation context, instead of repeatedly summarizing history. The researchers report a 27.7% average cost reduction across four agent benchmarks. Free and open source.
MisconceptDialogue finds that reported scores for AI math tutors can shift sharply based on the evaluation criteria used. That matters because detecting a wrong answer is not the same as diagnosing the student’s misconception and correcting it over a real conversation.
Lula’s election result is political news, not an AI product or research release. It has no specific AI tool, model, or pricing change attached to it.
OpenAI and Anthropic have cut some LLM prices by as much as 80% in a matter of months, putting pressure on rivals and lowering the cost of building AI products. Cheaper inference changes what agents, high-volume apps, and smaller teams can afford to run.
Dolly Parton’s Imagination Library is a book-gifting program, not an AI launch or model release. No specific AI product or pricing change is part of this story.
TACS-RF uses an LLM coding agent to drive professional circuit-design tools for RF and millimeter-wave hardware. It is a concrete test of whether agents can handle engineering workflows where designs must satisfy physical constraints, not just produce plausible code.
This paper argues that examples chosen by matching a sentence’s part-of-speech pattern can improve LLM dependency parsing more than examples chosen by semantic similarity. It is a useful reminder that the best context for structured language tasks is not always the most topically similar text.
arXiv:2610.31863v1 Announce Type: new Abstract: If agricultural machinery is to support intelligent, offline assistance, then the system must access technical documentation dynamically. In this paper, the Semantic Synchronization of operator manuals over ISO 11783-based CAN buses is analyzed, defined as the process that brings the local knowledge base of a tractor-side assistant into agreement with the operator manual of the connected implement. Data transfer performance was benchmarked using Markdown, JSON, and XML; various open-weight Large Language Models (LLMs) were also evaluated in cross
The mods at r/opensource posted new submission rules that, among other things, banned projects that use AI. The post made Saturday evening opens by saying, "If your project uses AI, is made by AI, or is intended for AI, it's not welcome on r/opensource. AI, as it exists today, is incongruent to this Open Source […]
arXiv:2610.31821v1 Announce Type: new Abstract: How well a poetry archive can be searched depends on what the user still remembers, not only on what the model understands. We show this for Classical Arabic poetry, benchmarking nine dense encoders and BM25 over two corpora, three indexing modes and two evaluation protocols. Which retriever wins is governed by how much of a poem's wording the query still carries. On tip-of-the-tongue queries BM25 leads every dense encoder, including an 8B state-of-the-art model (0.746 vs. 0.717 Recall@5); on abstract queries it falls to eighth out of ten (0.392
arXiv:2610.31898v1 Announce Type: new Abstract: Traffic safety videos provide rich evidence for understanding interactions between pedestrians and vehicles, but synthetic-to-real evaluation makes this setting challenging because models must learn from simulated scenes and generalize to real-world traffic. In this work, we present StateBridge, a traffic video-language framework designed to operate under this synthetic-only training protocol. Our approach uses phase-aware clip construction, pedestrian and vehicle track summaries, and structured prompts to ground vision-language models in stable
The footage looks okay, as long as you're not seeking artistry, inspiration, or human interest.
111 points, 52 comments
arXiv:2610.32024v1 Announce Type: new Abstract: Task instructions from users for agents are inherently underspecified, especially regarding safety boundaries--creating behavioral risks. To study these risks, we introduce BARC-Bench, a collection of 110 tasks across nine simulated workspace environments where benign requests leave openings for unsafe actions. In this benchmark, we evaluate agent behavior against safety boundaries established by workspace records, implicit environmental context, and unstated social norms. For example, an agent asked to schedule off-boarding discussions must arra
PersonaScienceBench adds 200 science-agent tasks that vary the simulated researcher, not just the research problem. It tests whether agents can adapt to different expertise, preferences, and working styles instead of succeeding with one fixed collaborator.
This paper proposes a framework for mapping how LLM agents behave across a full run, linking their actions to context and outcomes. Better behavioural analysis could make agents easier to debug, evaluate, and improve beyond a final pass or fail score.
Researchers argue that overall accuracy can hide flawed comparisons when testing how LLMs respond to different conversational pressure. The paper is a reminder that evaluation needs to validate the prompts and conditions, not only the model score.
Nanbeige released Nanbeige4.3-33B, a 33-billion-parameter coding model built for agent workflows, with a 512K-token context window and an Apache 2.0 license. It is free to download, giving builders another open model for long-running software tasks.
This paper uses multiple frozen models to reason about emotion in conversations containing text and images, then asks agents to verify their conclusions against evidence. The approach aims to improve multimodal emotion analysis without the cost of retraining a model.
Hugging Face released Carbon-X, a 14.8B model that reads raw DNA sequences and answers questions about them in plain English. It is free to download under Apache 2.0, bringing an open general-purpose language model closer to usable genomics work.
A study estimates that Europe’s summer heat wave killed more than 50,000 people in urban areas. It is a stark data point for AI systems used in climate modelling, public-health forecasting, and emergency planning.
Adobe v. ArtCraft is being watched as a test of how copyright law treats AI-generated or AI-assisted imagery and the tools behind it. The outcome could shape how image-model companies defend training data, outputs, and creator rights.
Researchers ask whether evolving an agent’s memory during a task can beat test-time training, where a model updates itself while solving a problem. If it works, scientific agents could improve through cheaper memory changes rather than costly online retraining.
This paper tackles finding unusual people in large image collections from text descriptions of their appearance and behaviour. It combines several retrieval models with a second ranking pass to narrow the gap between synthetic training data and real-world images.
Anthropic released OSS Scanner, a free opt-in service that uses Claude models to find vulnerabilities in critical open-source code and sends reports directly to maintainers. It puts frontier-model security scanning in the hands of projects that could not build it themselves.
Eunice is being teased ahead of launch, but the supplied listing does not say what the product does or when it arrives. Worth watching for details before drawing conclusions about its AI capabilities or pricing.
Lula has secured a fourth term as Brazil's president after a dramatic election comeback. The result could shape policy around AI regulation, platform rules, and digital infrastructure in Latin America's largest economy.
PaperFormalizer is a Lean-based agent pipeline that helps researchers turn papers into machine-checked proofs and catch missing assumptions or gaps. Formal verification could make published technical claims more auditable, starting with the authors themselves.
Permission to Retry studies what AI agents should do when a consequential tool action fails, its result is unclear, or the agent's permission has changed. Reliable recovery is a core problem for agents that can spend money, change records, or trigger real-world actions.
The Helpful Agent Problem examines how cooperating LLM agents can bypass intended review and approval workflows without acting maliciously. It is a warning that multi-agent systems can create safety failures through ordinary task completion, not just obvious jailbreaks.
CEMIR vs. FISER compares two ways for AI systems to infer what people mean when instructions are ambiguous: explicit reasoning versus adding contextual information. Better intent recognition matters for robots and agents that need to act on what users meant, not only what they literally said.
arXiv:2610.32069v1 Announce Type: new Abstract: Recent multimodal large language models (MLLMs) can perceive a traffic scene, reason about it, and propose a driving action within a single model. However, existing MLLM-based drivers reason from scratch at each frame and commit to a single explanation, discarding earlier hypotheses about the scene. When the scene is ambiguous, the agent locks in on the wrong interpretation and acts on it before evidence accumulates. In this paper, we propose DriveMind, an MLLM-based driving framework that keeps several hypotheses about the scene alive at the sam
arXiv:2610.32110v1 Announce Type: new Abstract: Agents and verifiers often rely on the text a tool prints, and that text can misrepresent what the tool actually did, even when the tool's computation is correct. We saw this happen in a production RAG research system used by the Pilsudski Institute in New York, where tool output formatters misled an agent into giving wrong or incomplete information to users. Each tool did its job correctly, but its output omitted search limits, result caps, or identifier namespaces. We also recreated an unlabeled estimate caught in code review before release. We
arXiv:2610.32107v1 Announce Type: new Abstract: Participating in a conversation involves not only deciding what to say but also whether to speak at all. This judgment carries even greater significance in environments like the Werewolf game, where participants' interests conflict, because both utterances and silence are subject to interpretation and assessment by others. Therefore, determining when to speak, as well as what to say, is crucial for an LLM agent playing the Werewolf game. In this study, we introduce a Werewolf AI agent developed for the Anytime Utterance Track of the AIWolfDial sh
Stanford and UC Berkeley researchers released TokenGate, a drop-in learned token pruning module for LLMs. It cuts long-context prefill time-to-first-token by 1.4x to 11.3x and peak memory by 1.5x to 3.4x without accuracy loss. The post Researchers from Stanford and UC Berkeley Introduce TokenGate: Token-Gated Attention Makes Long-Context Prefill Up to 11.3x Faster appeared first on MarkTechPost .
In Foreign Affairs, historian Niall Ferguson, researcher Lisa Klaassen, and former Google CEO Eric Schmidt cast the US and China as AI empires and every other country as a dependency. The race won't be decided by breakthroughs but by who can bankroll it. US corporations alone plan to spend more than $600 billion in 2026. Yet mounting debt, protests against data centers, and a potential chip crisis over Taiwan could stall both superpowers. The article Ex-Google CEO Eric Schmidt calls OpenAI and Anthropic modern colonial trading companies appeared first on The Decoder .
Smartphone agents don't get better by processing more data. They improve by training on as many different apps as possible. The researchers behind OpenMobile-2 built an open training environment with more than 110 applications, including 35 replicas of popular apps. On real Android devices, their model scores highest among open agents, the authors say. The article OpenMobile-2 proves open-source smartphone agents can rival proprietary models appeared first on The Decoder .
68 points, 28 comments
65 points, 68 comments
arXiv:2610.32114v1 Announce Type: new Abstract: One approach to accelerating progress on AI alignment is to automate parts of alignment research itself. Doing so requires systems that can perform reliably on hard-to-supervise research tasks, including identifying flaws in research proposals and evaluating whether a proposed experiment supports its intended claim. To evaluate this capability, we introduce Tampered ML, a benchmark of 1,858 item-pairs, each consisting of an experiment proposal reconstructed from a published machine learning paper and a minimally edited counterfactual that introdu
arXiv:2610.32126v1 Announce Type: new Abstract: RVL-CDIP is the de facto standard benchmark for document classification, and reported accuracy on it is now 98.8%. In this paper, we audit RVL-CDIP for data quality issues and find that approximately 10.5% of its 400,000 documents have erroneous labels and that 35.2% of the test set has a duplicate or near-duplicate in the training set. Identification codes on the pages are sufficient to classify at 69% accuracy, and a state-of-the-art model loses 11 points without them. Image-only models trained on RVL-CDIP lose 16 to 28 points on documents from
A new benchmark by P-Zero Research measures how well AI models design and evaluate experiments on their own. The results are improving so fast that Anthropic co-founder Jack Clark now thinks self-improving AI could arrive by 2027. The article Anthropic co-founder Jack Clark moves his prediction for self-improving AI forward to 2027 appeared first on The Decoder .
58 points, 85 comments
Jayden, Sam, and Zack are eagerly pitching editors on reported stories and investigative features. The catch? They're all AI agents.
SoftBank Group reportedly plans to invest $50 billion in Malaysia's technology sector, including artificial intelligence and data centers.
arXiv:2610.31701v1 Announce Type: new Abstract: Enterprise customer journeys are shaped not only by product experiences and marketing, but also by recurring resource-allocation decisions. In B2B go-to-market (GTM) organizations, territory carving determines which accounts receive seller coverage, which seller-account relationships are preserved, and how limited field capacity is distributed. Yet the process often remains manual, making it difficult to compare opportunities, value continuity, and evaluate tradeoffs among growth, balance, capacity, and feasibility. We present a production decisi
arXiv:2610.31789v1 Announce Type: new Abstract: Scientific computing increasingly relies on large language model-based agents for performance engineering tasks such as instrumentation, analysis, and workflow porting. However, agent effectiveness depends heavily on the surrounding harness, which typically relies on manually written skills and curated memory. This limits autonomous learning, forcing agents to repeatedly rediscover solutions rather than retain and reuse knowledge from prior executions. We introduce Phronix, a capability-centric learning paradigm that enables agentic harnesses to
arXiv:2610.31846v1 Announce Type: new Abstract: Intelligent agents should learn from their mistakes so that experience leads to continuous improvement. Yet today's agents recover from a mistake only temporarily: in-context reflections are easily forgotten across sessions and compete for limited context, while full-parameter fine-tuning is expensive and can interfere with existing capabilities. We introduce Self-Direction, a mechanism for agents to learn from mistakes that combines the benefits of temporary in-context retries and policy updates. When an agent fails a task, it reflects on its ow
arXiv:2610.31918v1 Announce Type: new Abstract: Distilling interactive LLM agents requires choosing between teacher-generated demonstrations and student-generated experience, corresponding broadly to off-policy and on-policy distillation. While both have proven effective, we argue that this distinction is inherently hierarchical: the policy that induces an interaction history need not be the same as the policy that generates the token prefix of the current action. We introduce a hierarchical agent distillation framework that formalizes these two levels of on-policiness and enables explicit con
arXiv:2610.31971v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly participate in negotiations on behalf of individuals and organizations, yet they remain vulnerable to manipulation, particularly in repeated multi-party interactions. Classical multi-agent systems address this challenge through explicit trust and reputation models, whereas existing work on LLM negotiation largely treats trust as an emergent phenomenon. We investigate whether LLM agents can form useful trust estimates from prior interactions and leverage them to improve future negotiation outcomes. We
arXiv:2610.32026v1 Announce Type: new Abstract: Conventional neural-network training typically follows a predefined optimization recipe, motivating a LLM agent that monitors training dynamics and adjusts optimizer settings as training progresses, but a fixed agent policy does not accumulate experience across runs. We introduce RSI-Opt, a plug-and-play Recursive Self-Improvement Optimizer Agents which evolves the knowledge that guides this process: a prompt, a playbook of procedures, a library of tested optimizer code, and a decision cadence. A meta-agent rewrites this policy using evidence fro
arXiv:2610.32045v1 Announce Type: new Abstract: AI-for-Science agents are increasingly being used for formulating scientific hypotheses, running autonomous experiments, and interpreting evidence. These agents are constructed by integrating a large language model (LLM) into a domain-specific agent harness, which in turn can improve the LLM's scientific capability and enable the model to orchestrate scientific tools. In this work, we ask whether model safety is preserved when an LLM is integrated in an AI-for-Science agent harness. We evaluate five agent harnesses on 50 harmful chemistry request
arXiv:2610.32060v1 Announce Type: new Abstract: Multi-agent LLM systems aim to improve reasoning through debate, evidence sharing, and mutual correction, but interaction can also introduce failures that final accuracy alone cannot explain. Agents may converge too early, ignore dissent, suppress correct minority answers, or preserve a wrong framing across rounds. We present a corpus-grounded taxonomy of communication failures, built from 3,211 failed multi-round trajectories across medical QA, coding, finance, multi-hop reasoning, ethics, and embodied text tasks. Using this taxonomy, we analyze
arXiv:2610.32181v1 Announce Type: new Abstract: Large language models (LLMs) are increasingly used to support decisions under incomplete information, where deciding what to look up and when to stop matters as much as the final answer. However, LLMs fall into an information trap: They pay for checks, i.e., costly pieces of evidence, that cannot change their decision or are not worth their cost, and they keep asking when the evidence is already sufficient. To this end, we propose Decision-Informed Value-of-information Acquisition (DIVA), a simple yet effective procedure for LLM information seeki
arXiv:2610.31683v1 Announce Type: new Abstract: Persona-conditioned language models are used as simulated patients and affective agents, but there is no validated way to ask whether a belief assigned to such a model organizes its behaviour or whether the model has merely adopted a mood. We present the Schema Coherence Inventory (SCI): 96 forced-choice items at eight levels of Beck's cognitive model, each offering six replies keyed to three negative self-schemas and their positive counterparts, scored by length-normalized log-likelihood so that nothing is generated or parsed. Affect alone can m
arXiv:2610.31840v1 Announce Type: new Abstract: Tool-calling agents are mostly optimized for accuracy; in deployment, however, their growing cost is latency: every tool call is decoded autoregressively. Speculative decoding accelerates this without changing the output, and retrieved past calls make strong drafts under real use, where each user's behavior tends to repeat. Yet existing systems keep no cross-session memory, or build the draft store once and never update it. We propose SpecMem, a tool-call memory kept alive and managed throughout deployment: verified calls are written back online
arXiv:2610.31874v1 Announce Type: new Abstract: Computational framing analysis is dominated by supervised classifiers that require large annotated corpora and predefined frame taxonomies, limiting their use for non-English languages, novel events, and low-resource settings. This paper presents Association Rule Mining (ARM) as an unsupervised, annotation-free framework for news framing detection. Each document or sentence is treated as a transaction of lexical items, and high-lift co-occurrence rules are read as candidate framing signals (Gamson and Modigliani's condensing symbols), confirmed i
arXiv:2610.32002v1 Announce Type: new Abstract: The rapid growth of microdramas has increased demand for high-quality script production. Long-form microdrama script generation with LLMs requires both diverse plot progression and long-range narrative coherence. Our observations reveal that candidate continuations increasingly concentrate on fewer narrative mechanisms as generation progresses, while conventional retrieval may reinforce trope reuse and overlook historical information essential to future plots. To address these challenges, we propose DraMaze, a multi-agent pipeline that explores d
arXiv:2610.31880v1 Announce Type: new Abstract: The Yangtze River Basin is a critical region for freshwater biodiversity conservation. The implementation of the Yangtze River Ten-Year Fishing Ban has increased the importance of effective aquatic biodiversity monitoring and species-level identification. However, fine-grained identification of closely related freshwater fish species remains challenging due to subtle morphological differences. Existing deep learning methods often struggle to balance classification accuracy and inference efficiency, while LLMs may generate unsupported information
arXiv:2610.31983v1 Announce Type: new Abstract: Video encoder models are increasingly used as world models for physical AI, where a policy plans by rolling out the latent, so what physics the latent holds bounds what the agent can plan over. Moreover, existing interpretability work probes quantities belonging to a single object; the quantities that make physics relational, such as momentum transfer, restitution, and friction, are defined between bodies and have not been examined. We probe relational physics attributes in two frozen self-supervised video encoders (V-JEPA2 and VideoMAE) under tw
arXiv:2610.32120v1 Announce Type: new Abstract: Complex text-to-image requests remain difficult to satisfy reliably. Verifying and repairing an image can fix an individual output, but a successful repair does not necessarily carry over to other requests. We present ImageRSI, a framework that keeps the image generator frozen and combines three components: five-dimensional verification and repair within each task, an experience library shared across tasks, and offline updating of the judge. Candidate experiences enter the library only after paired A/B validation, and the judge is updated with su
Anthropic says companies should evaluate whether they can consolidate sub-agents into a single agent that loads context on demand. In one architecture comparison, the single-agent setup ran about twice as fast. Anthropic has published a migration kit on GitHub to help teams make the transition. The article Anthropic says one AI agent beats many subagents now that models can handle complexity alone appeared first on The Decoder .
arXiv:2610.31747v1 Announce Type: new Abstract: Existing 3D talking avatar methods mainly rely on parametric face models including 3DMM or FLAME, whose compact motion representations limit their ability to synthesize extreme expressions and to model accessories such as headwear. We introduce AudioLift, a two-stage framework that uses generated multi-view videos as the intermediate representation for audio-driven avatar generation without parametric face models or per-instance optimization. Our framework combines a synchronized multi-view video generation stage with an inconsistency-robust Gaus
arXiv:2610.31848v1 Announce Type: new Abstract: Imaging Flow Cytometry (IFC) generates large multispectral datasets, yet the need for expert annotations bottlenecks the development of supervised deep learning models. While Self-Supervised Learning (SSL) offers a solution, its application to IFC remains fragmented and lacks systematic benchmarking. To address this, we present IFC-SSL-Bench, a modular framework for evaluating SSL in IFC. We benchmark six SSL approaches across four backbones on two heterogeneous datasets. By assessing downstream classification performance across annotation regime
arXiv:2610.31950v1 Announce Type: new Abstract: Recent instruction-following image editors such as Flux.1-Kontext enable text-driven editing but lack fine-grained control over where and how much an edit is applied. Existing approaches provide either continuous but global control or spatially localized editing at a fixed strength. To our knowledge, none jointly supports explicit spatial localization, continuous per-region strength, and independent simultaneous control of multiple subjects. We present LOCI (LOcalized Continuous control for multi- subject Image editing). Given a multi-subject ima
arXiv:2610.31986v1 Announce Type: new Abstract: Vision language models (VLMs) have been increasingly adopted in consumer smart-home camera systems to interpret recorded videos and provide insights. However, video clips captured from smart-home cameras are different from curated web videos dominating existing VLM benchmarks. Smart-home video clips often do not contain subtitles or narration and are captured from different viewing angles, under various lighting conditions, and in diverse scenarios. However, the capabilities of the model in this domain, under sparse visual signals and limited con
arXiv:2610.31820v1 Announce Type: new Abstract: Reliable confidence estimation is essential for trustworthy deployment of large language models (LLMs), but dedicated confidence training stage requires additional data and compute. We ask a fundamental question: what prediction problem does confidence training actually solve? Inspired by prior work on external confidence estimation, we compare self- and external estimators of comparable capacity trained on the same (question, response, correctness) triples. We show that an external model trained solely on a policy's behavioral traces can estimat
30 points, 62 comments
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30 points, 1 comments
27 points, 16 comments
arXiv:2610.32058v1 Announce Type: new Abstract: Text-to-image (T2I) models have achieved remarkable progress in image quality and instruction following, prompting growing research interest in their capacity for deep understanding that goes beyond literal prompt matching. However, without an explicit visual scheme provided, it remains unclear and has not been systematically evaluated whether T2I models can autonomously translate abstract concepts lacking a fixed visual referent, such as "loyalty," into visual representations that are accurate, diverse, and distinguishable from closely related c
arXiv:2610.32163v1 Announce Type: new Abstract: Affective computing is an interdisciplinary field that studies computational methods that relate to or influence emotion. These methods have been applied to interactive media artworks, but they have focused on affect detection rather than affect generation. For affect generation, computationally creative methods need to be explored that have recently been driven by the use of Generative Adversarial Networks (GANs), a deep learning method. The experiment presented in this paper, Forging Emotions, explores the use of visual emotion datasets and the
arXiv:2610.31808v1 Announce Type: new Abstract: Patronizing and condescending language (PCL) is a form of harmful language that belittles and assumes pity for vulnerable groups, reinforcing negative societal stereotypes. However, unlike overt hate speech, it is often difficult to detect, even for human judges. This paper proposes a system for the automated detection of PCL. Our first approach uses static word embeddings and a convolutional neural network for classification, while our second approach uses pre-trained BERT embeddings fine-tuned for the PCL task. Our second approach was considera
arXiv:2610.32034v1 Announce Type: new Abstract: The growing population of orbital debris has intensified interest in active debris removal and on-orbit servicing, for which dual-arm space manipulators offer improved grasping stability and load distribution relative to single-arm systems. However, coordinating two manipulators under inter-arm collision avoidance, orientation alignment, and sensing uncertainty remains challenging. This paper develops a coordinated reinforcement learning framework for cooperative motion planning of a dual-arm space manipulator in a target-reaching task with inter
arXiv:2610.31682v1 Announce Type: new Abstract: Conversation success is important for communication, social bonds, and well-being and is often reduced in autistic-neurotypical conversations. Previous studies have predicted perceived conversation success using acoustic and lexical features in pediatric autism-enriched samples, or using nonverbal behavior in non-autistic group interactions. Other work has characterized prosodic, facial, motor, and turn-taking synchrony in autistic interactions, but has primarily targeted diagnostic classification or group differences. The present study instead p
arXiv:2610.31686v1 Announce Type: new Abstract: Activation interventions can make a language model produce a target answer, but can the model use the intended condition in other computations? We study this question in Qwen3.5-27B on tool-use tasks where time or permissions determine eligibility and selection. Across 32 fresh worlds, two case-specific residual-writing variants raise familiar target-selection correctness from 35/128 to 119/128 and from 33/128 to 114/128. Yet these gains disappear when familiar facts and identifiers are recombined within the same selection task, while selection o
arXiv:2610.31687v1 Announce Type: new Abstract: Large language models are increasingly used to simulate and probe human behavior, e.g., for surveys. For these applications, a key property is that outputs satisfy distributional alignment: to match its output distribution with some reference (in this case, human) distribution. An obstacle in achieving distributional alignment is generalizing from small training data. In this work, we study (1) how to distributionally align LLMs; and (2) how aligned LLMs might generalize to unseen samples and unrelated topics and tasks. Using two large-scale benc
arXiv:2610.31911v1 Announce Type: new Abstract: As large language models (LLMs) process increasingly long sequences, attention becomes a major computational bottleneck. Sparse attention mostly targets inference, yet many methods still train densely or cannot support sparse training, and their added complexity makes sparsity hard to turn into wall-clock speedups, leaving dense attention widely used in practice. This paper introduces LazyFlash, a forward-dense, backward-sparse attention method built on the observation that attention training is dominated by the backward pass. LazyFlash keeps the
arXiv:2610.32042v1 Announce Type: new Abstract: Cross-lingual dubbing aims to convert source speech into target-language speech that remains synchronized with the original video. The key to this task is to produce a faithful translation that is naturally speakable within the source timing, which is non-trivial because languages differ in duration, rhythm, and articulation. In this paper, we propose X-Dubbing, which formulates the task as a joint problem of time-aware speech-to-text translation and video-aware text-to-speech. Specifically, our method consists of a Dubbing Translator that genera
arXiv:2610.32109v1 Announce Type: new Abstract: Language models can answer a question from a supplied document or from their parametric knowledge. When later asked where the answer came from, what determines their source report? We study this by intervening on the model's key-value (KV) cache after the answer is generated and before the source question is asked. On a controlled factual task with four models (7B-32B), changing or removing the document's cache never changes the report. In contrast, transplanting the question-and-answer (Q+A) cache between runs with identical question and answer
arXiv:2610.31734v1 Announce Type: new Abstract: A live esports broadcast needs a director who chooses which camera goes on air and a caster who talks over that choice, every second and without knowing what comes next. Streaming video language models have learned to commentate, but only over a broadcast that a human director has already produced. We study live broadcast production, in which a model receives the raw camera of every player and must both direct and commentate from the past alone. We present DirCast, a single streaming video language model that does both. Every second it scans all
arXiv:2610.31902v1 Announce Type: new Abstract: Objectives: In the area of digital pathology, nucleus segmentation is an essential step for quantitative analysis; however, high nuclear density and staining heterogeneity in histology images pose a great challenge. Methods: This study implements a lightweight Attention U-Net model on the MoNuSeg 2018 dataset to overcome these challenges. The proposed pipeline integrates comprehensive image and mask preprocessing, including stain deconvolution and multi-pass mask smoothing, with a high-fidelity overlapping patch-stitching strategy during inferenc
arXiv:2610.32013v1 Announce Type: new Abstract: This paper studies the problem of test-time robustness scaling for large vision-language models (LVLMs), which aims to mitigate language bias during inference. Existing approaches typically adopt test-time scaling techniques such as contrastive decoding to improve inference performance. Despite the progress, the performance of these approaches is far from satisfactory due to intentional evidence removal during perturbations and redundant inference cost. Towards this end, we propose a novel approach named Margin-gated Contrastive Debiasing with Ev
High-frequency traders like Jane Street and Hudson River Trading are now racing each other for AI computing power. The article Wall Street's fastest traders are building data centers and backing chip startups to stay ahead in AI appeared first on The Decoder .
Investors are already questioning the debt-fueled expansion of neoclouds and whether the large AI infrastructure build-out will generate long-term returns.
Toyota wants to turn itself into a global robotics company, and it's betting on humanoid robots and factory automation to get there. The article Toyota is betting $6.4 billion a year on humanoid robots and factory automation appeared first on The Decoder .
Coyotec is bilingual, swears, and tells listeners how to defend themselves against ICE. Its popularity comes as more broadcasters are experimenting with artificial personalities.
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arXiv:2610.31937v1 Announce Type: new Abstract: Modern Large Language Models (LLMs) remain vulnerable to carefully orchestrated multi-turn interactions that progressively bypass safety mechanisms. However, many existing red-teaming approaches rely on heuristics or impose strong assumptions that limit their applicability in fully black box settings. We address this gap by introducing $M^3$, an automated red-teaming framework that formalizes multi-turn jailbreaking as a finite-horizon Markov Decision Process (MDP). In $M^3$, an attacker LLM interacts with a target LLM while operating over a comp
arXiv:2610.32057v1 Announce Type: new Abstract: We present HyScale, a long-context training system for learned sparse attention. Learned sparse attention is a promising architecture for million-token contexts, widely adopted in production since DeepSeek-V3.2. Selection, however, grows into the training bottleneck. For each query, it scores every previous token whereas sparse attention reads only $k$; worse, current systems materialize a full score table and repeat selection during recomputation. Our key insight is that the output of selection is not an activation, but rather a set of indices.
arXiv:2610.32096v1 Announce Type: new Abstract: Diffusion language models (DLMs) support bidirectional denoising and iterative refinement, but generation remains largely controlled by token-level masking schedules. This leaves draft-level refinement implicit and limits explicit control over when to insert missing content, delete redundant spans, revisit uncertain regions, or stop editing. We introduce Diffusion Editing Language Adapter (DELA), a plug-in framework that enables controllable draft refinement with a frozen DLM. DELA operates on clean, variable-length drafts using four explicit edi
arXiv:2610.32099v1 Announce Type: new Abstract: Fixed source superposition and frequency partitioning constrain how communication content interacts with a probing reference in integrated sensing and communication (ISAC). We learn nonlinear waveform fusion across scales for image transmission and localization of multiple targets in range and angle. Reception uses the information available at each end. Adaptive temporal feature weighting and spectral updates combine image features and a deterministic linearly frequency-modulated reference in a shared complex state, generating a waveform with fix
arXiv:2610.32127v1 Announce Type: new Abstract: Post-training quantization (PTQ) is a key technique for efficiently deploying large language models, as it compresses weights into low-bit representations without retraining. Existing PTQ solvers, including GPTQ, LDLQ, and block coordinate descent, commit each weight block to a single codeword before its neighbors are considered, leaving near-optimal alternatives unexplored. In this work, we cast row-wise PTQ as posterior inference over discrete codebooks, in which existing solvers correspond to a zero-temperature, hard-decision limit retaining o
arXiv:2610.32177v1 Announce Type: new Abstract: Humans reason about the physical world by flexibly combining different forms of inference as a problem unfolds. Achieving this flexibility is a major open challenge in AI: capabilities often remain separated across specialized models, limiting how they can be composed and jointly adapted to a task. We introduce $\Psi$-0.6, a unified vision-language world model that adaptively composes perception, language, geometry, and simulation within a shared reasoning process we call streams of thought. These streams interleave images, text, and explicit rep
arXiv:2610.32193v1 Announce Type: new Abstract: Users often exhibit changes in interaction behavior before explicitly seeking assistance, enabling systems to anticipate future assistance use. This is especially important in high-stakes environments, where delayed assistance can have operational, financial, or safety consequences. However, conventional Large Language Models (LLMs) are often unsuitable for continuous on-device behavioral prediction because of their computational and memory demands. Behavioral patterns are also highly individual and device-specific, motivating local processing th
arXiv:2610.31681v1 Announce Type: new Abstract: Structured generation is typically formulated as a deterministic mapping from input to a single structured output. This assumption is fundamentally misaligned with natural language, which often admits multiple structurally distinct yet semantically valid interpretations. As a result, existing methods rely on single-reference supervision that collapses ambiguity into one prediction, leading to unstable and inconsistent outputs under underspecified inputs. We reformulate structured generation as an \textit{ambiguity-aware inference problem}, where
arXiv:2610.31684v1 Announce Type: new Abstract: Knowledge Graphs (KGs) have been popularized during the last few decades and are increasingly constructed and consumed by Large Language Models (LLMs). Various applications therefore rely on KGs to provide concise, complete, accurate, faithful, fresh, and traceable answers. However, what is the quality of those KGs? In which cases should a KG be used, and can it be trusted to ground an LLM? How might it be evaluated? We reviewed the literature on quality assessment (QA) of data, information, linked data, KGs, and the intersection of KGs and LLMs.
arXiv:2610.31723v1 Announce Type: new Abstract: This article introduces Latin Modern BERT (LAMB), an encoder for the Latin language based on the ModernBERT architecture, pretrained in two phases on more than 74B Latin tokens using a novel morphologically informed masking regimen. We also present two corpora for the Latin language. Acervus contains 24B BERT WordPiece tokens, including over 760K samples from heavily damaged OCR transcriptions, repaired by a ByT5 model trained for Latin text repair. The second dataset, Corpus Trinum, contains a mixture of over 100M strong and weak Latin language
arXiv:2610.31833v1 Announce Type: new Abstract: LawZero's Scientist AI trains a Predictor toward the Bayesian posterior, with an objective that penalizes inconsistent probabilities among latent explanations. We ask whether current frontier language models, with no such training, are already consistent in this sense zero-shot: do their probability judgments satisfy the law of total probability, $P(X)=\sum_z P(z)P(X|z)$, when the latent $z$ must be inferred from records in the context? In separate single-turn calls, models are asked for $P(X)$, for $P(z)$ over the latent's states, and for $P(X|z
arXiv:2610.31842v1 Announce Type: new Abstract: How do children learn the meanings of the simple, concrete words that form a majority of their early vocabulary? Statistical information about many word meanings can be recovered from the gradual accumulation of information about the co-occurrence of words with their referents, a proposal known as ``cross-situational learning''. Contrastive vision--language models allow this learning strategy to be tested on egocentric video data capturing children's early real-life experiences with visual and linguistic inputs. Initial successes of this method o
arXiv:2610.31894v1 Announce Type: new Abstract: Recent Sparse Autoencoders (SAEs) decompose the representations of large language models (LLMs) into interpretable features, enabling precise readout and control of encoded knowledge. However, existing SAEs treat each feature as a standalone unit, whereas real-world knowledge is often taxonomically organized. As a result, readout carries no guarantee of taxonomic consistency, and control lacks the granularity to specify the scope of an edit. We propose TaxoSAE, which explicitly encodes taxonomical information, built on is-a (hypernym-hyponym) rel
arXiv:2610.32055v1 Announce Type: new Abstract: Large language models (LLMs) often exhibit sycophancy, altering their answers to align with user suggestions even on objective factual and reasoning tasks. Yet the quantitative form of this influence--- and whether it is calibrated to source reliability--- remains poorly understood. In this work, we investigate this behavior through hint-induced answer shifts on MMLU questions, evaluating prompts of the form ``[Source] thinks the answer is (X)''. We systematically vary the source to which the hint is attributed (a professor, a friend, or a dog) a
arXiv:2610.32115v1 Announce Type: new Abstract: Sparse Mixture-of-Experts (MoE) is a powerful paradigm for scaling Neural Machine Translation (NMT) and managing language heterogeneity. However, practical limitations hinder its deployment on resource-constrained devices: the entire expert pool must remain in memory, and the per-token compute budget is statically fixed during training by rigid top-k routing. We introduce EDWARD-NMT (Elastic Dimensions With Adaptive Routing and Dual-consistency for NMT), a novel MoE encoder-decoder architecture designed for a train once, deploy anywhere paradigm
arXiv:2610.31765v1 Announce Type: new Abstract: Generating multiple videos of a dynamic scene from a single reference video requires synthesizing both observed and unobserved content consistently across viewpoints. Existing camera-control methods for video diffusion typically rely on architectural mechanisms, including camera embeddings, cross-view attention, and geometric conditioning. Although effective for static scene content, these approaches often struggle with the fine-grained, dynamic geometry that must remain coherent across views and over time. We synchronize the diffusion process it
arXiv:2610.31797v1 Announce Type: new Abstract: Estimating a camera pose from 3D-2D correspondences - Perspective-n-Point - is one of the most fundamental problems in computer vision. Existing methods either solve a minimal problem or replace the objective with an easier one. Surprisingly, no scalable certifiable solver for the actual Maximum Likelihood Estimation (MLE) exists. We propose PnP*, the first practical solver that guarantees efficient computation and returns a certifiable globally optimal solution. We prove that the underlying convex relaxation is exact under moderate noise, which
arXiv:2610.31803v1 Announce Type: new Abstract: Generating physically stable 3D structures from visual specifications requires resolving geometric ambiguity while accounting for physical interactions among objects. We present StackGen3D, a framework for generating stable block assemblies conditioned on a given block inventory and three orthographic silhouettes. Our approach consists of two stages. First, we develop a physics-informed graph neural network (PI-GNN) that models support relations between blocks and serves as a fast stability predictor for curating a diverse dataset of stable struc
arXiv:2610.31835v1 Announce Type: new Abstract: Temporary traffic control redirects vehicles through work zones, requiring automated vehicles to connect an interpretation of the visible devices to a continuous passing path. Geometric predictors can provide several plausible paths, yet selecting the one consistent with the temporary control remains difficult. We propose SAGE (Semantic-to-Action Grounded Execution), a read-decide-execute-check framework that separates path construction, path preference, and control compliance within a shared candidate space. The central idea is to use a vision-l
arXiv:2610.31908v1 Announce Type: new Abstract: We present PixGR, an efficient framework that integrates lightweight pixel-space generative modeling into feed-forward 3D Gaussian reconstruction to model scene uncertainty. Context observations are encoded once into a scene latent representation, which conditions generative decoding from noisy target views to produce a shared 3D Gaussian representation. We formulate this process using pixel-space flow matching, enabling end-to-end optimization of 3D reconstruction with direct pixel-space generative supervision through differentiable rendering. T
arXiv:2610.31951v1 Announce Type: new Abstract: Surgical point tracking requires persistent correspondence through tissue deformation and occlusion, yet dense trajectory annotations are costly to obtain. We present STEP, a two-stage framework that learns from surgical video and sparse physical endpoints. First, a frozen offline teacher generates candidate trajectories, and temporal reversibility and stereo consistency select dense supervision for a causal student. Second, a robust physical endpoint loss anchors terminal correspondence, while a round-trip loss constrains temporal consistency. A
arXiv:2610.31979v1 Announce Type: new Abstract: Deepfakes increasingly undermine trust in digital media, yet existing detectors often struggle to generalize across unseen manipulation techniques, compression levels, and dataset shifts. Most approaches rely on RGB appearance, frequency-domain artifacts, or their combination, learning cues present in the final synthesized image. While effective in-distribution, such cues are frequently generator-specific and can diminish as synthesis models evolve. We argue that a complementary perspective can be obtained through implicit neural representations
arXiv:2610.32041v1 Announce Type: new Abstract: Vision Transformers (ViTs) have demonstrated strong performance in many computer vision tasks but do not naturally extend beyond the natural image domain. One important example is Fourier-space data, which is used to represent a rich and diverse set of modalities, including medical imaging and microscopy. Standard ViT tokenization partitions an image into square patches on a Cartesian grid, which poorly fit the frequency and phase structure of Fourier-space data. We introduce Non-Uniform ViT, a simple, general-purpose vision transformer that rede
arXiv:2610.32059v1 Announce Type: new Abstract: Diffusion-based image generation models produce high-quality images but may generate harmful or policy-violating content, motivating machine unlearning (MU) to selectively remove target concepts while preserving general capabilities. However, existing MU approaches often struggle with the distributed and entangled nature of concept representations, leading to inefficient optimization and imprecise edits when applied through large-scale fine-tuning over full diffusion trajectories. To bridge this gap, we propose Concept Acupuncturist (ConceptACP),
arXiv:2610.32075v1 Announce Type: new Abstract: Feed-forward neural renderers synthesize global illumination across novel 3D scenes without per-scene optimization or runtime ray tracing. Existing transformer-based renderers tokenize individual triangles, so self-attention scales quadratically with mesh resolution. Yet macroscopic radiative transport is governed by the underlying surface geometry and object arrangements rather than a particular mesh discretization; finer tessellation can thus inflate token counts without changing the underlying transport physics, motivating a discretization-agn
arXiv:2610.32132v1 Announce Type: new Abstract: Proactive streaming video understanding requires assistants to track unfolding events and respond when a user's requested condition is met. To make these decisions, assistants must accumulate relevant evidence and reassess response conditions as new observations arrive. Existing work offers complementary capabilities for this process: proactive video assistants learn when to respond, while streaming video reasoning methods generate scheduled intermediate text to organize observations and support subsequent answers. Together, these capabilities pr
arXiv:2610.32146v1 Announce Type: new Abstract: Hyperbolic vision-language models are well suited to modeling the hierarchical structure of multimodal data, because hyperbolic volume grows exponentially with radius, enabling low-distortion representations of tree-like taxonomies from general to specific concepts. However, existing pretraining objectives for hyperbolic vision-language models entangle semantic alignment with entailment modeling, forcing radial coordinates to simultaneously encode semantic matching and hierarchical structure, leading to conflicting geometric constraints. In this
arXiv:2610.32174v1 Announce Type: new Abstract: Automated monitoring of dairy cows increasingly measures one part of the body, such as the rump for temperature or the back for posture, yet the region itself is usually placed by hand. Existing systems either score the cow as a whole or detect sparse keypoints, often in controlled walkways, and neither yields the named region masks that such measurements need on a crowded commercial rotary platform. We present a perception front end that finds the target cow, ten bony landmarks and four named dorsal regions (withers, back, rump and tail-head) in
arXiv:2610.32182v1 Announce Type: new Abstract: Vision-Language Models (VLMs) are widely deployed, yet the internal mechanisms behind their safety behavior remain poorly understood. We present a lightweight inference-time framework for localizing and manipulating safety-sensitive representations in frozen VLMs, without retraining or modifying parameters. On Qwen3-VL-4B, layer-wise activation divergence analysis reveals non-uniform safety geometry across depth. Causal interventions show that early vision layers support strong bidirectional refusal control through activation steering along a lea
"Despite massive investments and widespread hype around artificial intelligence, the technology has delivered zero financial payoff to corporate America outside of tech."
We compiled a list of all the hottest AI wearables, including Muse Charm, Bee, Friend, Plaud, and more.
This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more about AI's impact on the web, follow Emma Roth. The Stepback arrives in our subscribers' inboxes on Sunday at 8AM ET. Opt in for The Stepback here. How it started The digital publishing industry has relied on […]
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Paris Marx, author of Hyperscale, has been tracking global data center development for years. The US has a lot to learn from what’s happened everywhere from Ireland to New Zealand.
OpenAI is poaching Anthropic's customers with steep discounts. Companies like Rippling, Replit, and DataCamp say they've cut AI costs by up to 90 percent after switching. Anthropic's growth has slowed, but OpenAI is trading its own margins for market share. Chinese rivals like Deepseek are piling on more price pressure. The article OpenAI offers discounts up to 90 percent to poach Anthropic's biggest customers appeared first on The Decoder .
Structured knowledge retrieval acquires task-relevant evidence from structured knowledge sources for downstream tasks. Existing methods rely on unconstrained retrieval or predefined query-rule supervision, such as author-paper rules for scientific discovery and anatomy-drug rules for precision medicine. Such supervision is rarely available in practice. We propose Co-Adaptive Rule Routing and Constraining Retrieval (CoRE), which jointly learns which reasoning rule to assign to each query and how to retrieve evidence under that rule, without predefined query-rule pairs. A routing agent selects a
100+ AI Agents, Agent Skills and RAG Apps - Free and Open Source.
VoiceStudio is the open-source, fully-local ElevenLabs alternative — voice cloning, voice design, video dubbing, dictation, transcription & audiobook creation in 646 languages.
image-to-video by Lightricks. 2,117,147 downloads, 9371 likes.
The growth of time-series datasets and foundation models has made dataset comparison a central part of time-series workflows. Assessing source-target transfer before model adaptation requires similarity estimates that can summarize transfer-related dataset relationships without exhaustive source-target evaluation over every candidate pair. Many metrics have been proposed for measuring similarity between time-series datasets, but they capture different dataset relationships and vary in how well they align with downstream utility across tasks. We evaluate this gap by measuring the correlation be
Experiential is the open source, zero markup gateway for BYOK, self-hosted and 1000+ marketplace models. It learns from your traffic to cut costs, recommend better models, and train a specialized model you own.
UserArena is a benchmark for testing shopping agents against simulated users over long, multi-step interactions. Better user simulations could make it easier to train and evaluate recommendation agents before exposing them to real customers. Free research resource.
TradingAgents is an open-source framework that splits financial trading work across specialist LLM agents for research, debate, and execution. It is another practical example of multi-agent orchestration moving from demos into reusable tooling. Free to download.
MiniMax released MiniMax-M3.1-Flash, an open image-and-text model now trending on Hugging Face. It gives developers another downloadable multimodal model to test for visual question answering and image-grounded workflows. Free to download.
RSIHub is an open-source framework for building and comparing agent-evolution systems, which use execution feedback to improve prompts, tools, and orchestration. Shared infrastructure could make this fast-moving area easier to reproduce and benchmark. Free to use.
A coding-agent skill for multi-phase security audits with independently verified, machine-readable findings
Edit videos with coding agents
image-to-image by black-forest-labs. 700,652 downloads, 1636 likes.
text-to-speech by fishaudio. 23,216 downloads, 1019 likes.
World Models (WMs) can support Multimodal Agents (MAs) through simulated experience, while Agent trajectories can inform World Model refinement. Evaluating this interaction requires separate evidence about action selection and state evolution. We introduce Dual Exam, which tests Agents under fixed environment response mechanisms and World Models under fixed action interventions across physical, digital, social, and scientific domains. The Agent track compares 10 configurations on 1,088 items in 15 reporting categories. The World Model track contains 400 cases across eight scenarios; 15 configu
Qwen3.8-27B has crossed 7.6 million downloads on Hugging Face, making it one of the platform's most-used image-and-text models. It is free to download, and that level of adoption makes it a serious open-weight option for multimodal work.
DeepSeek-V4.1-Flash-1017 is trending on Hugging Face as an image-and-text model, with nearly 96,000 downloads. The open-weight release is free to download and gives developers a widely adopted multimodal DeepSeek checkpoint to test locally.
TenetBench is a Russian-language benchmark for testing whether language models follow specific professional ethics rules, rather than broad safety categories. It gives researchers a more concrete way to measure whether an AI assistant could quietly violate a user's professional code.
Building virtual embodied agents that operate in open-ended games like human players is a long-standing goal requiring both long-horizon autonomy and reliable native-interface control. As a procedurally generated open world, Minecraft provides a natural testbed for studying the integration of these capabilities. Existing agents typically provide either native visuomotor control without long-horizon autonomy or high-level planning executed through environment APIs or predefined skills. To bridge this gap, we propose HiSTEVE, a hierarchical VLA agent with a capability-aware agent harness. At the
Slider-based control aims to give users continuous, fine-grained control over the intensity of individual edits in instruction-based image editing. However, existing approaches fail to deliver reliable continuous controllability: traversing a slider can produce abrupt visual jumps and non-monotonic changes, while jointly controlled sliders can interfere with one another. To systematically study these limitations, we introduce SlideBench, a comprehensive benchmark for continuous image editing spanning diverse editing categories and equipped with a unified suite of metrics quantifying monotonic
Location encoders map geographic coordinates to high-dimensional descriptors through contrastive alignment with paired Earth Observation imagery. We prove that this construction is structurally limited: any Lipschitz location encoder produces an output of intrinsic dimension at most 2, regardless of architecture or bandwidth, which forces the contrastive loss to strictly exceed its asymptotic optimum. Empirically, joint training also collapses the encoder's embedding covariance to low rank. Although dimension-limited, current location encoders sidestep this spectral collapse by training agains
We introduce RynnUnify, a 16B unified embodied model that combines strong embodied understanding with long-horizon visual planning through interleaved video--language reasoning. With capabilities spanning spatial reasoning, physical prediction, and grounding, RynnUnify connects an understanding of the physical world with visual anticipation of how it evolves across successive subtasks. Building on these capabilities, we further develop an agentic extension through reinforcement learning to selectively use video predictions for reasoning about unobserved states and interaction outcomes. We also
Open multimodal reasoning models have benefited from large-scale reasoning supervision, yet reliable post-training remains challenging due to uneven data quality, inefficient supervision construction, imbalanced difficulty, and cross-domain interference. We introduce MMVistaReason (MVR), an open-data post-training recipe with three components: (1) broader capability coverage across complementary Analytical and Real-World reasoning groups, emphasizing structured reasoning versus visual perception and spatial grounding; (2) efficient SFT and RL data construction, standardizing heterogeneous open
The rapid growth of microdramas has increased demand for high-quality script production. Long-form microdrama script generation with LLMs requires both diverse plot progression and long-range narrative coherence. Our observations reveal that candidate continuations increasingly concentrate on fewer narrative mechanisms as generation progresses, while conventional retrieval may reinforce trope reuse and overlook historical information essential to future plots. To address these challenges, we propose DraMaze, a multi-agent pipeline that explores diverse causal paths through trope memory-guided
In a hierarchical multi-agent system, a Leader assigns tasks and subagents carry them out. Jointly training the Leader and the subagents is difficult because a final answer gives only indirect feedback to the subagents. An LLM judge can score the performance of the subagents, but these scores depend on the judge's own assessment of the quality of subagents' responses. We present MultiAgent-OPD, which combines on-policy distillation with reinforcement learning to jointly train a shared-parameter Leader-Searcher-Answerer team end to end. We first train a Teacher in a Searcher-Answerer workflow u
Music source separation (MSS) has been conventionally addressed by discriminative methods. Recently, flow matching has emerged as a strong generative paradigm for modeling complex data distributions, with successful applications across diverse domains. These advances motivate us to systematically investigate flow matching for MSS in a four-source (Vocals, Bass, Drums, Other) setting in full-band stereo. Specifically, we investigate prediction targets, flow paths, single-source versus multi-source modeling, and mixture consistency constraints. Beyond the signal-level metrics commonly used for M
Video generation models are promising foundations for embodied world models, but only if their generated rollouts are physically valid and faithful to task instructions. Multimodal large language models (MLLMs) are increasingly used to judge these properties, yet their agreement with human judgments on embodied videos has not been systematically evaluated. To address this gap, we introduce RoboJudge-Bench, a benchmark for evaluating MLLMs as judges of embodied video generation. The benchmark measures two fundamental dimensions, Physical Adherence and Instruction Alignment, across 800 human-ann
On-policy self-distillation enables post-training foundation models without relying on stronger external teachers. However, existing approaches rely on ground-truth question-answer pairs, human-curated problem sets, or other supervised signals. In this work, we reformulate on-policy self-distillation as a self-training framework, driven by self-generated question-answer pairs and privileged reasoning demonstrations. We push supervision to the limit: guided solely by coarse, high-level topic descriptions, the model synthesizes its own curriculum of queries and privileged reasoning traces withou
Developing general-purpose algorithms with global asymptotic convergence guarantees for variational inequalities (VIs) remains a longstanding challenge. This paper proposes a path-following framework for finite-dimensional VIs with arbitrary continuous mappings and polytope domains. The approach first transforms a general VI into a smooth VI on a simplex. Its key innovation is to formulate the resulting problem on a fiber bundle, called the fixed-point bundle, whose geometric structure enables the systematic integration of initialization, path-following, and singularity avoidance. Without impo
Developing general-purpose algorithms with global asymptotic convergence guarantees for Nash equilibria of general-sum games remains a longstanding challenge. This paper extends the fixed-point bundle framework for finite-dimensional variational inequalities (VIs) from simplex domains to product-of-simplex domains, enabling its direct application to Nash equilibrium computation. The resulting fixed-point bundle exhibits a composite fiber bundle structure. The key innovation is to construct an equivalent VI on a simplex and establish the equivalence between the two fixed-point bundle frameworks
Single-image 3D scene construction is a fundamental problem in 3D vision. Recent methods such as SAM-3Dfy enable end-to-end reconstruction of 3D assets and their scene layouts, but still struggle with fine-grained scene geometry and asset quality. Meanwhile, specialized 3D object generators provide detailed object geometry, while 3D foundation models offer strong and generalizable spatial understanding. We propose Easy-3Dfy, a compositional workflow that decouples object generation from scene layout estimation, allowing specialized models to fully exploit their respective strengths. An RPF-bas
Video foundation models have advanced rapidly in understanding and generating visual sequences, while video world models seek to capture the underlying dynamics of the world they depict. Despite their different objectives, both concern the relationship between video observations and an evolving world. This survey adopts world modeling as a unifying lens for examining these developments. Viewing an observed video as a partial, lossy, and temporally ordered projection of a latent temporal world, we organize the literature around four complementary operations: reading infers world properties from
We introduce TWIG (Tree-structured Weights with Inherited Gates), a representation in which every weight of a neural network is the end of a path of small discrete decisions: a sign, a shift by a power of two, a zero gate, an index into a shared table. Weights that belong together share the upper part of their paths, and each decision is inherited by every weight below it, so it is stored once and applied once. This simple change has three consequences. Any trained network can be written as a TWIG, and when the decisions are powers of two its weight layers become circuits made almost entirely
Recognizing task completion and failure enables robots to stop upon success and initiate recovery or replanning after failure. Existing methods augment Vision Language Action (VLA) policies with task status signal prediction to support these execution decisions. However, learning and integrating these signals can require fine-grained supervision, external monitoring models, or complex architectural designs. Moreover, the potential of task status signals to directly guide action generation remains underexplored. We present Echo, a framework that combines task status signal learning without fram
AI-generated text detectors are increasingly deployed in consequential settings---peer review, education, admissions, journalism, and content moderation---where their outputs directly affect individuals. Yet the strongest detectors are fine-tuned neural classifiers whose decisions are opaque, while transparent alternatives (stylometric and linguistic-feature classifiers) underperform them by wide margins. We present a pipeline that converts a fine-tuned neural detector into a classifier that operates on human-readable concepts. We accomplish this by training sparse autoencoders (SAEs) directly
Fine-grained multimodal intelligence requires models to both understand a designated region and localize it from language. Existing methods learn these directions with separate objectives and task-specific annotations. We present Generalized CycleGRPO, the first unified reinforcement learning framework coupling region understanding and localization across spatial, temporal, and spatio-temporal domains. Given a region, a multimodal large language model generates candidate descriptions and then localizes each description to reconstruct the input region. Reconstruction overlap provides a verifiab
Language models can make personalized recommendations from rich item content, user history, and feedback. However, evaluating many candidates with full-text inputs incurs substantial online computation and limits serving throughput. We introduce DuoLM, a framework for fast and effective personalized recommendation that integrates a compact recommendation model with an efficient serving runtime. An encoder LLM learns compact item representations for offline reuse, while a personalized ranker LLM combines them with user history and feedback for recommendation. We train both models end to end wit
What ran today and what it cost.