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Researchers introduce EAPO, an exploration-aware reinforcement learning framework that enables LLM agents to selectively explore uncertain scenarios before acting. The method uses fine-grained reward functions and adaptive exploration mechanisms to improve decision-making across text and GUI-based agent benchmarks.
Researchers have developed a geometric framework for understanding how large language models process information across their layers, identifying three distinct phases in next-token prediction: Seeding Multiplexing, Hoisting Overriding, and Focal Convergence. The study reveals that model depth primarily increases capacity for candidate disambiguation rather than adding fundamentally new computational stages.
Researchers developed CT-IDP, a quantitative phenotyping framework that uses organ segmentation and derived descriptors to classify abdominal CT diseases through interpretable logistic regression. The approach achieved superior performance compared to vision-transformer baselines across multiple datasets, demonstrating the value of explainable AI in medical imaging.
Researchers introduce STEMO-Bench, a benchmark for evaluating video understanding in multimodal large language models (MLLMs), and propose STEMO-Track, a framework that reduces hallucinations by explicitly tracking object identities and states across time. The work addresses a critical limitation in current video AI systems: their inability to persistently monitor objects and temporal relationships in dynamic scenes.
Researchers propose Shapley regression, a game-theoretic machine learning method for diagnosing APDS, a rare genetic immune disorder. The approach combines interpretability with predictive power by modeling symptom interactions while maintaining transparency, validated on both public datasets and a real-world cohort of 222 patients.
Researchers have optimized Alpamayo 1, a reasoning-based autonomous driving system, by redesigning it from multi-reasoning to single-reasoning architecture while accelerating diffusion-based action generation. The optimization achieves a 69.23% latency reduction while maintaining trajectory diversity and prediction quality, demonstrating that system-level efficiency improvements are critical for practical autonomous driving deployment.
Researchers introduce PnP-Corrector, a framework that improves long-term forecasting for coupled dynamical systems by separating error correction from physics simulation. The method achieves 29% error reduction in 300-day ocean-atmosphere forecasts by training a correction agent to counteract systematic biases that accumulate when multiple interacting systems compound prediction errors.
Researchers propose Safety Internal (SInternal), a framework that trains large reasoning models to verify the safety of their own outputs rather than relying on external compliance mechanisms. The approach demonstrates that models can internalize safety understanding through verification tasks, significantly improving robustness against adversarial jailbreaks and out-of-domain attacks.
Researchers demonstrate that Fourier Neural Operators (FNOs) used for PDE simulation can be formally verified using SMT solvers by exploiting their piecewise-linear structure once weights are fixed. While exact encoding provides sound proofs and counterexamples on small models, scalability remains limited, revealing a fundamental tradeoff between formal verification rigor and practical applicability for production neural operators.
Researchers introduce OPT-BENCH, a benchmark evaluating whether large language models can self-improve through iterative feedback in complex problem spaces. Testing 19 LLMs across machine learning and NP-hard problems reveals that while stronger models adapt better, even the most advanced systems remain constrained by their base capabilities and fall short of human expert performance.
Researchers introduce Ace-Skill, a co-evolutionary framework that improves multimodal AI agents by optimizing both data sampling and knowledge organization. The system achieves 35% performance gains on tool-use benchmarks and enables smaller models to inherit capabilities from larger ones without additional training.
Researchers introduce M³ (Multi-scale Morton Measure), a framework that improves neural surrogate models for physical simulations by addressing training bias from discretized data sampling. The method achieves up to 4.7× error reduction in volumetric cases and maintains superior performance even with 90% data reduction, demonstrating that data distribution strategy significantly impacts operator learning efficiency.
Researchers resolve an open problem in multi-armed bandit theory by characterizing how best-action oracle queries improve learning algorithms in the realistic bandit-feedback model. They prove that benefits depend critically on reward structure: correlated stochastic rewards cannot achieve the theoretical gains seen in full-feedback settings, while i.i.d. stochastic rewards maintain near-optimal improvements with logarithmic precision.
Researchers propose IMAX, a framework that uses trainable prefix tuning to improve exploration in reinforcement learning with verifiable rewards (RLVR) for language model reasoning. The approach addresses entropy collapse by creating diverse reasoning trajectories, achieving performance gains up to 11.60% in Pass@4 accuracy across multiple model scales.
Researchers introduced a benchmark testing whether vision-language model (VLM) agents can recognize themselves in mirrors, a cognitive capability that emerges only in some animal species. Results show self-identification through reflection occurs mainly in stronger VLMs, while weaker models fail to extract self-relevant information despite viewing their reflections, revealing that language-based self-reference alone does not guarantee grounded self-understanding.
Researchers introduce FRACTAL, a novel state space model architecture that integrates fractional measure theory to improve long-sequence modeling by balancing short-term sensitivity with long-term memory retention. The approach achieves 87.11% on the Long Range Arena benchmark, outperforming existing SSM models like S5, addressing a fundamental trade-off in temporal sequence analysis.
Researchers propose Structure-Centric Graph Foundation Models (SCGFM), a novel approach that treats graph topology as the primary source of transferable knowledge using geometric bases and Gromov-Wasserstein distances. The method addresses key limitations in existing graph foundation models by handling structural heterogeneity and incompatible node feature spaces, demonstrating improved generalization across both in-domain and cross-domain graph tasks.
Researchers demonstrate that execution-based voting methods for LLM code generation significantly outperform text-based majority voting by 18-52 percentage points. The study reveals that input quality—particularly sketch-based generation—matters far more than the aggregation algorithm itself, challenging assumptions about how to select optimal code outputs.
A new position paper argues that despite functioning as useful co-scientists, agentic AI systems are fundamentally not designed for truly autonomous scientific discovery due to challenges in problem selection bias, insufficient tacit knowledge in training data, compressed output diversity, and lack of real-world experimental feedback loops.
Researchers introduce VIGIL, an evaluation framework that separately measures whether embodied AI agents correctly complete tasks and properly report success, rather than conflating execution failures with commitment failures. Testing across 20 models reveals significant performance gaps in terminal commitment despite similar task execution, highlighting a critical blind spot in current AI agent benchmarking.
Researchers introduce Hi-MoE, a hierarchical Mixture-of-Experts framework that addresses a fundamental routing trade-off in sparse MoE models by implementing two-stage optimization: inter-group load balancing and intra-group expert specialization. Tested on large-scale NLP and vision tasks, Hi-MoE achieves 5.6% perplexity improvements and superior expert balance compared to existing methods.
Researchers present CaTR, a reinforcement learning framework that optimizes real-time taxiway routing and conflict avoidance for multiple aircraft at airports. The system uses hierarchical traffic representation and value-decomposed learning to balance safety and efficiency, demonstrating superior performance compared to traditional planning and optimization methods while maintaining practical computational speed.
Researchers prove that primacy effects, anchoring, and order-dependence are mathematically inevitable in autoregressive language models due to causal masking constraints. The findings are validated across 12 frontier LLMs and confirmed through human experiments, suggesting cognitive biases represent resource-rational responses to sequential processing rather than design flaws.
Researchers establish formal mathematical bounds for when human-AI teams outperform individuals, proving complementarity occurs only when error correlation between humans and AI falls below a critical threshold. The framework explains why 70% of real-world human-AI collaborations fail to achieve synergy and provides predictive formulas validated against human datasets.
Researchers present Gate-and-Merge, a zero-shot framework enabling vision-language models to recognize and compose multiple user-defined concepts without requiring co-occurrence training data. The approach uses lightweight LoRA adapters for individual concepts and employs a gating mechanism to merge them intelligently at inference time, maintaining concept integrity while enabling compositional personalization.
