The paper introduces Straggler-Aware Group Control (SAGC), a dynamic group-size controller that optimizes synchronous on-policy RL training by adapting group size to minimize delays caused by slow rol…

This paper proves that Random Reshuffling in Shuffling Stochastic Gradient Descent dominates vanilla SGD in smooth convex optimization after any finite number of epochs.

FuzzAgent introduces a multi-agent, evolutionary system that significantly improves library fuzzing by iteratively refining the test suite based on runtime feedback, achieving superior coverage and bu…

The paper proposes an energy-efficient drag reduction strategy for turbulent flows by combining Multi-Agent Deep Reinforcement Learning with SHAP-guided explainable deep learning, achieving superior p…

This study re-evaluates LLM package hallucination rates on a new cohort of frontier models, finding a significant reduction in overall hallucination rates but identifying a persistent, model-agnostic…

This paper explores five methods for making a linked list work efficiently with multiple threads, from using one big lock to a lock-free design.

The paper introduces SNARE, a novel adaptive benchmarking pipeline that systematically measures overeager behavior in coding agents, finding that the agent framework accounts for the majority of the v…

The paper introduces SNARE, a novel adaptive testing pipeline that systematically measures overeager behavior in coding agents, finding that the agent framework accounts for the majority of the variat…

This paper introduces DiStash, a disaggregated transactional key-value store that enables an application to use a single transaction to manage key-value pairs across different pools of stashes, preven…

The paper introduces SlotGCG, an improved jailbreak attack method that systematically searches for the most vulnerable token insertion positions (slots) within a prompt, significantly boosting attack…

The paper introduces SARDI, a novel, training-free framework that uses low-confidence 'lookahead' tokens generated during the denoising process of discrete diffusion language models to dynamically gui…

This paper introduces Stigmergic Graph Memory (SGM), a method to improve warehouse throughput in many-to-many Multi-Agent Pickup and Delivery (MAPD) by using a bounded, decaying memory layer to record…

The paper proposes BiRD, a bidirectional ranking defense mechanism that enhances the robustness of Retrieval-Augmented Generation (RAG) against adversarial attacks by analyzing the alignment between f…

The paper argues that the standard Attack Success Rate (ASR) metric for LLM jailbreaks is unstable and systematically inflated, proposing new frameworks to account for stochasticity in both evaluation…

The paper introduces a novel record-and-replay detection mechanism to accurately detect the true avalanche effect in ransomware, achieving high accuracy against real-world samples.

Stochastic Lifting is a novel technique that enhances the modeling of stochastic physical systems by introducing independent random labels to state transitions, allowing a single network to generate d…

The paper identifies 'memory-induced tool-drift,' a systematic vulnerability where personality biases stored in an LLM agent's memory silently corrupt tool-calling decisions, even when those biases ar…

The paper characterizes 'dead-entry' TLB misses in GPUs, which occur when recently evicted translations are immediately re-walked, and proposes DEPOT, a Bloom filter mechanism that significantly reduc…

This paper provides the first non-vacuous generalization analysis for the Stochastic Variance Reduced Gradient (SVRG) method by establishing sharp, data-dependent algorithmic stability bounds, thereby…

The paper introduces OverEager-Gen, a new benchmark that measures 'overeager actions'—where coding agents perform unauthorized tasks beyond a benign request—and finds that removing explicit consent de…