Model releases, architecture work and prompting research on large language models — from frontier-lab announcements to the arXiv papers behind them.
Large language model agents have been used to search over symbolic structures such as programs and equations. We propose CueRator, an agentic framework for policy-aware decision-rule discovery, which...
As LLMs increasingly assist in moral reasoning, omission bias, the tendency to prefer inaction even when equivalent framings reverse substantive outcomes, poses a significant risk of skewed decision-m...
Multi-agent systems (MAS) powered by large language models have shown strong performance across code generation, mathematical reasoning, and question answering. However, existing methods for automatin...
This paper establishes a theoretical framework for vertical adaptive layer skipping, proving three foundational results: (i) an Expected FLOPs formula (theorem 2) giving a closed-form expression for t...
In clinical practice, agreement among independent experts is treated as evidence of reliability, and multi-round consensus has become a core mechanism of agentic medical question-answering systems. Wh...
The article explains the transition from the original Cowork system, which ran model inference and tool calls in a local Anthropic VM, to a new cloud‑based version that hosts both inference and the VM. The new design isolates each session in its own sandbox, eliminates local resource drain, and allows file access through the desktop app, addressing concerns about battery usage, performance, and continuity when a laptop is closed.
S2PD: Serial-to-Parallel Diffusion for Physically and Logically Consistent Video Generation introduces a hybrid diffusion approach that first applies autoregressive diffusion at high noise levels and then switches to parallel diffusion at low noise levels. This method coordinates interdependent events to produce valid state transitions while reducing sampling time compared to fully serial generation. Implemented with a pixel‑space diffusion transformer and a LoRA‑fine‑tuned pretrained video model, S2PD outperforms bidirectional baselines in rule adherence and achieves greater temporal stability and sampling efficiency across games, physical simulations, and real video.
The paper highlights that AI voice assistants using ASR and LLMs struggle with regional British accents because most ASR models are trained on American English. It introduces CavaBench, a benchmark of spoken financial queries, to evaluate ASR models and their impact on downstream tool‑calling accuracy across British accents. The study finds that while WER predicts tool‑calling accuracy, it may not fully capture task‑level performance, revealing accent‑related failures that vary by model and acoustic conditions.
The paper investigates how frontier AI systems perform on tasks related to cyber security, specifically exploit generation, vulnerability repair, and subsequent attacks, using five nonpublic software environments. It evaluates both open‑weight and proprietary models, employing deterministic graders rather than LLM judges to score performance. Results show significant variation across systems and vulnerability types, with repair scores higher than attack scores in two environments and lower in three, and highlight that passing an initial security test does not guarantee long‑term defense, as 92 of 524 defender test intervals still experienced successful exploits after the first one was stopped.
The paper introduces VERA, an automated framework that audits large language model (LLM) reasoning in software vulnerability analysis. Instead of relying on free‑form explanations, VERA requires models to produce a Structured Reasoning Record (SRR) that captures pointers, memory operations, and state transitions in machine‑readable fields. A multi‑stage judge then checks each SRR against eight reasoning failure modes, revealing that reasoning flaws are as common in correct verdicts as in incorrect ones and that VERA detects 87% of errors missed by free‑form LLM‑as‑judge evaluations.
GraphDecide is a model‑independent benchmark designed to evaluate System One models—such as Jev—that make decisions directly from supplied options on graph‑related tasks. The benchmark combines structural task profiles, matched graph‑text input contrasts, and heuristic‑proposal controls to diagnose graph decision performance. In testing fourteen model‑interface configurations, GraphDecide shows that accurate adjacency recognition does not guarantee broader structural correctness, joint graph‑text input does not consistently improve prediction, and feasible construction does not ensure high solution quality.
The paper introduces DUCB-OGD, an algorithm that couples a Discounted Upper‑Confidence‑Bound sampler with Online Gradient Descent to address dynamic minimax regret in robust large‑language‑model post‑training. It operates under instantaneous mini‑batch‑only bandit feedback, tracking worst‑source performance without re‑evaluating historical data. Experiments on fine‑tuning, preference optimization, and reinforcement learning demonstrate that DUCB‑OGD improves worst‑group robustness with negligible computational overhead.
DeferKV rethinks when to evict key‑value (KV) cache entries in long‑context large language models. By delaying eviction until the first decoding step and combining prompt‑side and decode‑side attention signals, it aligns KV importance estimation with actual generation needs. The method requires no extra training or modules and consistently improves performance on benchmarks while keeping latency low.
The paper introduces Guidance‑TTT, a method that separates strategic planning from execution in test‑time training for large language models. A small guidance model is trained at test time to propose high‑level changes, while a frozen, larger execution model implements these changes, reducing the cost of maintaining gradients and optimizer states. Guidance‑TTT achieves strong results across four domains—combinatorial optimization, heuristic programming, machine learning, and GPU kernel optimization—outperforming prior work and matching state‑of‑the‑art leaderboard scores.
Anlu introduces counterfactual supervision for in‑context time series anomaly detection, pairing each query with two contrasting reference records to enforce reference‑dependent learning. By adding a reference memory and gated adapters to a frozen time‑series foundation model, Anlu improves the mean VUS‑PR from 0.542 to 0.607 on 350 evaluation sequences. Replacing the reference with zeros drops performance to 0.499, highlighting the importance of reference conditioning.
The study evaluates an auditable patient‑timeline reconstruction system that tracks provenance, records revisions, and refuses to answer when evidence is missing. Using a synthetic corpus of 1,000 patients and 3,353 notes, two provenance‑aware Evidence Graph operators reduced graph size by 33–37% while preserving all answers across 6,813 query points; a fixed‑window baseline failed to answer over half of the points. The system’s evidence‑gating mechanisms (BioClinicalBERT and a zero‑shot LLM) responded appropriately to evidence‑unavailable controls, but performance varied on marker‑free controls, with BERT maintaining high accuracy but the LLM’s coverage dropping sharply. "whyItMatters":"The results demonstrate that provenance‑aware evidence graphs can significantly reduce data complexity while maintaining answer integrity, highlighting a practical approach to building auditable clinical NLP systems."
MS-Exam-Gen is a reproducible framework that builds a source‑grounded multiple‑choice question benchmark for evaluating large language models on knowledge about multiple sclerosis MRI. The pipeline uses expert‑indexed sources, topic induction, evidence‑grounded MCQ generation, automated quality audits, and consistency checks to produce a 3,058‑item benchmark covering 16 topics and 53 subtopics. Evaluation of 12 LLM endpoints on this benchmark revealed a wide accuracy range (89.7% to 46.9%) and identified items frequently missed by models, while audits showed reduced answer cues and position‑sensitivity in scoring.
OpenAI has launched a new visual advertising format within ChatGPT, enhancing how ads are presented to users. The update also expands measurement tools, establishes attribution partnerships, and improves brand suitability options for advertisers.
The paper introduces TrustMI, a method to causally control how large language model assistants decide to trust their users. By creating 2,000 contrastive conversations that vary in ability, benevolence, and integrity, the authors learn steering matrices that adjust trust decisions along linear directions in model activations while keeping the model parameters frozen. Experiments across six instruction‑tuned models show that these steering changes reliably alter trust decisions and affect safety‑related behaviors such as compliance with harmful requests, prompt injections, and insider threats.