WEQA introduces a query-adaptive agent framework that combines language models with specialized wearable data analysis tools. It outperforms LLM and agentic baselines by 24% in accuracy and demonstrates improved usefulness and clinical soundness in expert and user evaluations.
LEADS proposes a framework that uses an LLM agent to discover hybrid cardiac electrophysiology models through an iterative reasoning-and-action loop. It formulates domain knowledge as a structured action space, enabling physically grounded, interpretable, and numerically stable model designs, outperforming both human-designed and other LLM-based approaches on synthetic and real cardiac data.
ReAge3D introduces a framework for realistic and identity-preserving 3D face re-aging. It uses a 2D diffusion model and center-out editing to ensure multi-view consistency, preserving fine age-related details through masked diffusion and view reconstruction.
An empirical study of 86,156 test-file patches from 33,596 agent-authored PRs reveals that 80.2% of test patches contain weak or no explicit oracle signals. Strong-oracle test files significantly improve merge likelihood (OR = 1.28, p < 0.001) after adjusting for multiple factors, indicating test file presence alone overestimates verification strength.
IUU+DB is a large language model-driven system that tracks illegal, unreported, and unregulated fishing, seafood fraud, and labor abuse. It extracts key data elements from diverse documents, classifies relevant incidents, and enables trend analysis to identify geographic and behavioral hotspots. The system supports research, risk assessments, and policy enforcement in fisheries and supply chains.
DRFLOW introduces a benchmark to evaluate agents' ability to predict personalized workflows from heterogeneous sources. It includes 100 tasks across five domains, grounded in 3,900 sources and featuring 1,246 reference workflow steps. DRFLOW-Agent achieves up to 10.02% F1 improvement over baselines, yet significant challenges remain in accurate workflow prediction.
A red-team study of Anthropic's Fable 5 and Opus 4.8 models reveals both are vulnerable to adaptive iterative attacks, with Opus 4.8 breached on 11.5% of harmful intents and Fable -5 on 6.1%. Despite robust defenses, both models generated 1,620 and 702 panel-confirmed harmful completions across all harm categories, automatically and efficiently under automated attack.
A policy learning technique using imitation learning is proposed to predict red agent actions in partially observable cyber environments. The method learns red agent policies from network observations and defender actions, enabling neurosymbolic cyber-defense agents to accurately predict attacks and adapt defenses in diverse simulated scenarios.
EvolveNav introduces a self-evolving framework for zero-shot object-goal navigation that improves during test time. It uses a rule memory derived from past trajectories and a confidence-based retrieval strategy to select effective actions, reducing redundant exploration. The method achieves a 10.1% higher success rate than existing baselines with fewer unnecessary steps.
ReproRepo introduces a scalable framework using GitHub issues to evaluate ML paper reproducibility. It shows that LLM agents like Codex with GPT-5.5 identify at least one blocker in 90% of paper-repository pairs without executing code, though exact localization remains challenging.
VERITAS introduces a generator-verifier framework that enables robots to improve policies in real time without additional training. A visual verifier evaluates actions at inference time, allowing consistent performance gains through verified rollouts that serve as effective supervision for offline policy improvement. Post-training with these verified rollouts matches expert demonstrations in efficiency, without human intervention.
A new approach called negative token filtering enables stable single-rollout training by preventing false penalties on negative samples. The method improves performance on agentic tasks compared to group-based RL techniques, while matching group-based methods on reasoning tasks.
Two studies show conflicting results on LVLMs' ability to coordinate efficient referring expressions. Explicit prompting enables models to achieve efficient communication, but implicit prompting fails to trigger this behavior, revealing fundamental differences in human-AI communication.
NarrativeWorldBench evaluates 21 LLMs on nine narrative-structure metrics across horizons of 10 to 200 episodes, with cross-lingual support in Hindi, Tamil, Telugu, and Marathi. N-VSSM, a latent world model using Mamba-2, achieves plot-beat F1 of at least 0.84 across all horizons with 4x lower compute than closed-frontier models and outperforms Claude Opus 4.5 in long-arc consistency and controllability in a professional writer study.
Well-known brands dominate LLM recommendations by 100% when products are identical, but this advantage vanishes with a mere +0.1-star rating edge. Authority-style marketing claims, such as fabricated clinical evidence, break this dominance at a bias surplus of +0.17 rating points, with models responding differently. A social dilemma emerges in multi-brand competition, where collective optimization reduces individual payoff from +0.802 to +0.007 and eliminates recommendations for non-participating brands.
MODE-RAG proposes a multi-agent system using Variational Free Energy to dynamically gate interventions and reduce cross-modal hallucinations in retrieval-augmented generation. It integrates Monte Carlo Tree Search and logit perturbations to address causal fabrications and sycophancy, with dedicated agents ensuring factual verification and formatting stability. Evaluated via ModeVent, a subset of MultiVent, the system significantly improves robustness against logical fabrications.
PARSE reduces prompt injection attack success from 25.4% to 15.6% on real enterprise documents across five professional domains, with statistically significant improvement (p=0.014) and 86.9% utility. It outperforms paraphrasing and uses provenance-aware sanitization to preserve factual content while routing most documents through a lightweight path.
AIPatient Arena evaluates large language models in end-to-end clinical consultations using EHR-grounded patient-specific knowledge graphs. It assesses LLMs across eight clinical competence dimensions, revealing strong performance in interview skills, ethics, and explanation clarity, but persistent weaknesses in handling ambiguity, information coverage, and diagnostic reasoning, with process failures like repetitive questioning and omitted history.
As enterprise agent tool catalogs scale from 10 to 110 agents, routing accuracy drops 16--23 percentage points on under-specified requests. An oracle analysis identifies retrieval and confusion gaps, with embedding-based shortlisting recovering +10--11pp F1. A human-annotated study of 1,435 utterances confirms real-world recovery of +10--17pp despite lower absolute performance.
Large language models outperformed humans and supervised models in next speaker prediction using the AMI corpus, despite lacking audio-visual data and domain training. Multimodal LLMs surpassed text-based LLMs in addressee and turn-change detection but still fell short of human performance, highlighting challenges in utilizing raw audio-visual signals. Ablation studies show conversational context is crucial, especially for next speaker prediction, with both humans and LLMs struggling during frequent turn changes.