A new multi-agent system, Multi-Agent Fictitious Play (MAFP), addresses stance entanglement in decision-making by modeling stakeholder perspectives as agents. MAFP uses game-theoretic fictitious play to iteratively improve decisions through mutual best responses, outperforming baselines on tournament strength and robustness in competitive scenarios.
Rubric-Conditioned Self-Distillation introduces a framework that uses structured rubrics to provide fine-grained, token-level feedback during self-distillation of reasoning language models. By conditioning teacher models on rubric-level criteria, it enables more precise credit assignment than scalar rewards, outperforming GRPO and OPSD by 1.0 and 0.9 points on average across science reasoning benchmarks.
Turing-RL introduces a reinforcement learning method using an LLM judge to evaluate how indistinguishable generated responses are from real user inputs. It outperforms baseline methods in both LLM and human evaluations across chat and Reddit forum domains, demonstrating that optimizing for indistinguishability improves user simulator performance.
OmniAgent introduces a POMDP-based iterative Observation-Thought-Action cycle for video understanding, enabling on-demand action execution to selectively distill audio-visual cues into persistent textual memory. It achieves state-of-the-art performance on ten benchmarks, with a 7B agent outperforming a 10× larger Qwen2.5-VL-72B model on LVBench (50.5% vs. 47.3%).
A new clustering framework uses a-contrario anomaly detection to define clusters as maximal subsets without anomalies under a null hypothesis of randomness. The Perception algorithm identifies outliers using an expectation-based threshold (\mathbb{E} < 1), enabling robust, parameter-free clustering that expands from minimal seed inputs and handles noise and emerging clusters effectively.
Skill-MAS introduces a new approach that decouples experience retention from parametric updates by modeling orchestration as an evolvable Meta-Skill. It uses a closed-loop process involving multi-trajectory rollouts and selective reflection to distill reusable strategy principles, achieving strong performance gains and robust transferability across tasks and LLMs.
This work introduces a framework that certifies vision-language model robustness under semantic-level transformations, using text prompts as proxies. It quantifies extent intervals for which predictions remain unchanged, without requiring additional data for each variation. Experiments on synthetic and real-world data demonstrate its effectiveness across diverse semantic variations.
TAPO advances self-distillation by constructing explicit micro-reflective trajectories that retain erroneous reasoning and insert natural-language diagnoses. These trajectories, derived from correct and incorrect model rollouts, provide fine-grained error corrections anchored in the model's own reasoning, improving both first-pass reasoning and error correction compared to GRPO.
A new method uses diffusion to train CRFs on entire label sequences, conditioning on noisy labels. When combined with approximate inference, it reduces POS-tagging error by 16.5%.
A study evaluates how architectural inductive biases affect machine learning emulators' ability to capture sudden stratospheric warming dynamics in idealized simulations. Results show that three-dimensional vertical coupling is a key bias, with model performance diverging significantly during active SSW-like variability. However, low forecast error does not ensure accurate wave-mean-flow interactions, as coherent errors persist in stratospheric wave-driving structure.
QUAM-SM is a post-hoc framework that uses adversarial search to identify 'adversarially fragile' pixels in medical image segmentation. It disentangles epistemic and aleatoric uncertainty and outperforms existing methods in reliability and boundary sensitivity on public datasets with expert annotations.
A study finds that excluding features based on manipulability alone is suboptimal in strategic classification. The research develops a joint algorithm for selecting features and tuning ridge regularization, offering a practical framework to mitigate strategic manipulation in healthcare decision systems.
A domain-shift aware neural network is proposed for estimating unbalance masses in rotating shafts under varying operating conditions. The model uses maximum mean discrepancy to align feature representations across different operational domains, improving prediction accuracy when system behaviors differ from training conditions. Results show its effectiveness in structural health monitoring applications where domain discrepancies are unknown or unaccounted for.
We propose a generative method using Latent SDEs to detect anomalies in sparse and irregular multivariate time series. The approach projects observed data onto continuous-time stochastic systems, handling missing values and irregular sampling while capturing cyclic patterns. Experiments on six benchmark datasets show our method achieves top performance, outperforming state-of-the-art baselines, especially under severe data sparsity.
REVES introduces a two-stage iterative framework that enhances LLM reasoning through sequential revision and verification. It achieves +6.5 points over RL baselines and +4.0 points over standard multi-turn training on LiveCodeBench, using a 4B base model with fewer rollouts than large evolutionary systems. The method improves error correction and generalizes to out-of-distribution puzzles like n_queens and mini_sudoku.
The paper proves that satisfiability of binarized neural networks is NP-complete by reducing it to SAT. It also shows that uniform image occlusion leads to a piecewise-constant output structure, allowing polynomial-time robustness verification.
A new framework enables zero-shot active feature acquisition by leveraging LLMs to elicit only discriminative statistics like unary deviations and pairwise co-variations. Using maximum-entropy closure, it resolves ambiguity in feature selection and outperforms LLMs itself, especially on challenging IBD patient cases where diagnostic uncertainty is high.
A new framework enables protein language models to generate controllable protein sequences without labeled data or wet-lab validation. It uses task-agnostic rewards based on model uncertainty and semantic consistency to guide generation, with Soft and Binarized Reward Optimization outperforming baselines in coverage and controllability across diverse conditions.
A new framework enables online reward-punishment learning without environment rewards, using only fixed-channel perceptual packets. It achieves high accuracy in value inference and policy optimization, with B_xi attaining 0.952 balanced reward-sign accuracy and overall policy performance reaching 0.979 optimal-action accuracy in tested tasks, outperforming controls like zero reward and shuffled targets.
EfficientRollout introduces a self-speculative decoding framework that reduces rollout and end-to-end latency by up to 19.6% and 12.7% respectively, without compromising final model quality. It uses a quantized drafter derived from the target model and integrates a system-aware toggle policy to avoid compute-bound regimes, enabling effective speculation during evolving policy generations.