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.
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.
Concatenating LLM-generated features to graph neural networks systematically reduces accuracy on homophilous benchmarks, with PubMed accuracy dropping by -17.0 ± 0.3 pp. This degradation is linked to LLM-alone discriminability (Delta_sig), which correlates strongly with concatenation cost (r² = 0.38) and shows a power law relationship with feature dimension and node count (r² = 0.97), particularly in low-Delta_sig, low-node scenarios.
OPD-Evolver introduces a slow-fast co-evolution framework that enables agents to select, act on, and reuse experience through on-policy self-distillation. It outperforms existing memory and training-based methods by up to 11.5% and 5.8% respectively, and demonstrates capability to challenge large-scale models like Qwen3.5-397B-A17B and Step-3.5-Flash.
SkillMigrator learns reusable web skills by matching layout structures instead of specific element references. It stores each skill as a transferable interaction pattern (TIP) with a structural sketch, enabling efficient skill reuse across sites. Compared to state-of-the-art methods, it reduces average LLM-action counts by 8-10% on WebArena and Mind2Web at matched success rates.
MambaCount introduces a spatial sparse state space duality block to enable efficient text-guided open-vocabulary object counting. It addresses causal modeling limitations and high entropy in spatial token responses, achieving state-of-the-art results on FSC-147 with a test MAE of 12.23 while maintaining linear complexity.
EnvRL introduces a framework that enhances agentic reinforcement learning by incorporating environment dynamics through state prediction and inverse dynamics objectives. It achieves significant gains in success rates on long-horizon benchmarks, improving Qwen-2.5-1.5B-Instruct performance from 72.8% to 77.4% on ALFWorld and from 56.8% to 67.0% on WebShop when trained with GRPO.
The LLM-as-Environment-Engineer framework uses LLMs to automatically redesign training environments in reinforcement learning by analyzing failure trajectories and contextual data. On the MAPF-FrozenLake testbed, it outperforms larger proprietary LLMs and fixed-environment baselines, with Qwen3-4B achieving the strongest aggregate performance. Analysis shows that failure evidence and preserved working configurations are key, and the current RL checkpoint performs better than the base model as an environment engineer.
SuCo introduces Minimal Sufficient CoT (MSC) as the shortest reasoning prefix adequate for correct answers. It employs a two-stage training framework—MSC-Aligned Fine-Tuning and Sufficiency-Aware Policy Optimization—to reduce reasoning length while maintaining or improving accuracy across math, code, and science tasks.
A causal audit shows that text-only models match multimodal models in chest radiography accuracy. Across nine systems, a text-only model performs within 5.7 points of the best multimodal model, and a 119-billion-parameter model is indistinguishable from a 7-billion-parameter text-only baseline. Grounding audits, not accuracy, should determine clinical deployment.
A new framework automates prompt refinement for LLM agents by splitting the observation-to-action pipeline into goal-conditioned and action selection modules. It uses an LLM-driven evolutionary loop to iteratively improve prompts based on environment feedback, achieving up to 72.5% success on PutNext where prior agents failed, without model fine-tuning.
Dynamic Rollout Editing (DRE) addresses overthinking in RL-trained reasoning models by modifying successful trajectories post-answer emergence. DRE preserves the correct reasoning prefix while editing unnecessary continuation, weakening the credit assigned to redundant thinking without penalizing valid reasoning. Experiments across diverse tasks demonstrate its effectiveness in reducing overthinking.
GLM-5.2 is the first open-weights model to achieve 80% accuracy on Terminal-Bench and outperforms all other available open models. It also surpasses Gemini, positioning it as a frontier-level model at a significantly lower cost.
LOGOS is a unified generative language model that represents scientific objects and their interactions as token sequences in a shared grammar. It achieves consistent or superior performance across diverse natural science tasks, demonstrating the feasibility of a single model serving multiple domains. The model scales positively with parameter count, and its design suggests that AI for Science should align deeply with large language models through shared architectures and training.
DeepRubric introduces a data construction framework that builds query-rubric pairs by first defining verifiable evaluation targets through an evidence tree. It generates 9K supervision examples and trains a 8B model with GRPO, achieving performance comparable to state-of-the-art models using 13x fewer RL GPU-hours.
KVEraser enables efficient localized context erasing in large language models by replacing only the KV cache states of an erased span with learned steering states. It achieves near-full-recomputation performance on in-domain tasks across 1K to 32K context lengths, with only a 24% latency increase, and outperforms other approximate methods in long-document QA with 3--4x speedup over full recomputation.
MetaSyn introduces a dataset of 442 expert-curated meta-analyses from Nature Portfolio. It evaluates twelve LLM agent configurations and reveals a critical bottleneck in study screening, where no system recovers more than 52.7% of ground-truth included literature despite high retrieval recall.
ContextRL introduces an indirect auxiliary objective to improve long-horizon reasoning and multimodal performance in LLMs. It rewards models for selecting the context that supports a query-answer pair, using contrastive context data from coding agent trajectories and image-based visual questions. ContextRL achieves +2.2% and +1.8% gains over standard methods on long-horizon and visual QA benchmarks, with gains attributed to the selection objective, not data augmentation.
Qwen3-8B internally tracks the value of its current trajectory, defined as the likelihood of achieving its goals. This 'value' axis distinguishes confidence levels, backtracking behavior, and code correctness, and shows that preference optimization boosts confidence in rewarded behaviors. The model assigns low value to politically sensitive queries post-training, and fine-tuning increases confidence within specific domains.