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2,173 papers
These findings demonstrate that current defense paradigms optimize for single-turn refusal benchmarks while rendering multi-step agents fundamentally unreliable, necessitating new approaches that preserve tool execution competence under adversarial conditions.
A consistency boundary analysis is presented that characterizes when diagonal short-memory SSMs can approximate causal attention and identifies structural gaps that remain and proposes InfoMamba, an attention-free hybrid architecture that consistently outperforms strong Transformer and SSM baselines.
Experiments on simulated and real-world benchmarks demonstrate that SCALE improves state-of-the-art VLAs and outperforms existing TTS methods while maintaining single-pass efficiency.
WorldMind is introduced, a framework that autonomously constructs a symbolic World Knowledge Repository by synthesizing environmental feedback that unifies Process Experience to enforce physical feasibility via prediction errors and Goal Experience to guide task optimality through successful trajectories.
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The stability of safety refusal decisions across random seeds and temperature settings is investigated by investigating the stability of safety refusal decisions across random seeds and temperature settings to demonstrate that single-shot safety evaluations are insufficient for reliable safety assessment and that evaluation protocols must account for stochastic variation in model behavior.
This work proposes Think-at-Hard (TaH), a looped transformer optimized for selective iteration that employs a lightweight neural decider to trigger latent iteration, only at tokens likely to be incorrect after the standard forward pass.
OceanGym is introduced, the first comprehensive benchmark for ocean underwater embodied agents, designed to advance AI in one of the most demanding real-world environments, and reveals substantial gaps between state-of-the-art MLLM-driven agents and human experts.
This work introduces a 98% automated pipeline to produce high-quality Quranic datasets and presents a novel ASR-based approach for pronunciation error detection utilizing the authors' custom Quran Phonetic Script (QPS) to encode Tajweed rules (unlike the IPA standard for Modern Standard Arabic).
Attention is widely understood as an associative memory, but that description alone does not predict how the memory will behave. Predictive theories do exist, but in the literature on animal learning. We show that the state updates of the major linear-attention families are term-for-term identical with named models from a century of animal learning theory: linear attention implements Hebbian contiguity, DeltaNet implements Rescorla--Wagner error correction, and decay variants such as RetNet implement contiguity with a stimulus trace. This dictionary turns conditioning phenomena into testable statements about the in-context behavior of linear transformers, while distinguishing algebraic consequences from empirical measurements. Algebraically, it yields an exact closed form for Kamin blocking, verified in simulation to $<10^{-7}$ across five learning rates. Empirically, it predicts a dissociation that survives training on generic in-context association: error-correcting attention exhibits cue competition, whereas contiguity-based attention does not. A single state also has two capacity regimes, with measured scaling exponents of 1.22 for faithful retrieval and 1.89 for identification, consistent with linear and near-quadratic predictions. Across the full head grid, retrieval error is governed primarily by total state size rather than its partition across heads, indicating that heads provide capacity rather than redundant copies. We also prove no spontaneous recovery for the analyzed single-state recurrences under cue-orthogonal retention trials; with a never-presented-cue control and probes within the trained positional range, we likewise find no recovery in trained models. Finally, we introduce PH-attention, a Pearce--Hall-inspired rule with an explicit feature-indexed associability state that yields cue-dependent learning rates and is absent from the token-computed gates we compare.
This paper designs Markov decision processes (MDPs) for different combinatorial problems and proposes to train conditional GFlowNets to sample from the solution space and demonstrates that GFlowNet policies can efficiently find high-quality solutions.
This survey reviews representative Transformer-based autonomous driving models and organizes them by task role, sensing configuration, and architectural design and analyzes how efficiency constraints reshaping model design choices in practice affects deployability, robustness, and safety.
RecourseBench is the first benchmark to explicitly ground recourse evaluation in structured claim verification and mathematically rigorous reproducibility standards, all while featuring the largest collection of state-of-the-art recourse algorithms (27 in total).
General Science
Climate & Sustainability
A new machine-learning framework aims to improve the success rate of computational protein design while moving away from results that reproduce sequences found in nature.
A new algorithm learns to anticipate the unprecedented scenarios that critical infrastructure and global supply chains are least prepared for.
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
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