Looped World Models (LoopWM) introduce a looped architecture that iteratively refines latent environment states using a parameter-shared transformer. This approach achieves up to 100x parameter efficiency over conventional world models by adapting computation depth to each prediction's complexity.
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.
The MLLP-VRAIN group submits a cascaded SimulST system using Parakeet and Qwen 3.5 models with adaptive black-box policies. For En→De, It, Zh, it employs ASR word-boosting and RAG with pre-translated exemplars in the new context track, achieving +5.82 XCOMET-XL improvement on MCIF En→De and an additional +1.03 gain via context integration.
This study evaluates Word2Vec's ability to capture semantic relationships in Toki Pona, a language with only 130 words. Using 1.4 million sentences, it finds that non-core tokens do not disrupt embedding structure and may actually bring similar words closer in vector space. The results show Word2Vec's effectiveness relies more on distributional patterns than vocabulary size, even at extreme lexical reduction.
SpeechDx introduces a large-scale benchmark with 12 datasets and 27 tasks across diverse health conditions. It evaluates models by speech production stages and reveals that large-scale models perform best, while domain-specific models show limited generalization across clinical conditions.
Large language models produce narratives that are more similar to each other than human-written stories. Frontier models converge on a generic narrative pattern, lacking the diversity found in human-authored stories. Common techniques like negative prompting and temperature scaling do not significantly reduce this homogeneity.
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.
A study challenges the assumption that visual attention signals reliability in vision-language models. It finds near-zero correlation between spatial attention and accuracy, showing instead that self-consistency across reasoning paths is a stronger predictor of truth. Reliability is better explained by generation dynamics and internal state distributions, not visual attention patterns.
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.
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.
STATEWITNESS introduces an activation explainer that audits deception in reasoning LLMs by reading hidden states and generating natural-language answers or structured reports. It achieves a 0.916 mean AUROC, outperforming existing black-box monitors and activation probes by 11.6% and 25.0% respectively, and provides query-level, schema, and evidence-level traces for human inspection.
A new study identifies second-order bias in large language models—social bias in their judgments about biased content. Using entitlement epistemology, the research develops a reasoning task to assess whether LLMs accept or reject biased texts based on demographics, revealing implicit biases that vary by target group and evade safety guardrails. The work introduces two metrics to quantify these biases and calls for more theoretically grounded evaluation methods in NLP.
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.
This paper analyzes deep transformer expressiveness using bounded-depth grammars. It constructs transformers with positional attention where model depth scales linearly with grammar depth, and neuron count grows quadratically with production rules. The results support the linear representation hypothesis by showing these models can encode abstract grammatical states in low-dimensional, linearly separable subspaces.
NAR-MBR decoding improves speech recognition by maximizing expected utility from sampled outputs of non-autoregressive models. It achieves better performance than prior NAR methods and runs faster than autoregressive decoding across multiple corpora.