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19,710 papers
Large Language Models (LLMs) treat all context tokens uniformly, lacking explicit separation between executable instructions and passive data. This leads to prompt injection vulnerabilities, ambiguous instruction binding, and unreliable execution control. We present a post-training methodology using LLMON (LLM Object N...
Effective firefighter training requires practice under dynamic and stressful emergency conditions, yet real-world live-fire exercises are costly, risky, and difficult to scale. Virtual Reality (VR) offers a safe and controllable alternative, but many existing VR firefighter training systems rely on fixed scenarios or p...
In recent years, warehouse logistics optimization has increasingly focused on intelligent, data-driven systems to enhance operational efficiency. Conventional static product placement strategies are being progressively replaced by dynamic slotting approaches that can adapt to real-time variations in warehouse condition...
This paper examines what can be reliably inferred when LLMs perform well on test sets produced by CLadder and finds that even simple models such as random forests perform well on CLadder-generated data sets after training on a sample of generated data.
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A novel framework that integrates sentiment and depression analysis with a rule-based filtering mechanism prior to model inference is proposed that aims to significantly improve the contextual relevance, empathy, and risk-aware of model-generated responses.
An expert-curated benchmark of 250 questions spanning qualitative reasoning, numerical calculation, symbolic derivation, and equation formulation for inertial confinement fusion is developed, noting that domain-specific performance can improve through specialization, but benchmark gains alone do not establish scientifi...
The results suggest that the opinions of healthcare professionals toward the causes of algorithmic bias, the criteria of algorithm assessment, the perceived fairness, and bias mitigation approaches may vary depending on their job roles, perspectives, tasks, and the algorithm characteristics.
GAUSS is presented, a workload-aware analytical and Monte-Carlo simulator that predicts the full distributions of time-to-first-token (TTFT) and time-between-tokens (TBT), together with the sustainable request rate, for a given workload, engine, and accelerator.
SkillMem is introduced, which represents each reusable skill as a small external latent memory and makes it directly available during latent multi-agent reasoning, showing that compact procedural memories can be reused without sacrificing the efficiency of latent multi-agent reasoning.
An evaluation framework is developed that distinguishes solution quality from execution failures, including numerical inaccuracies, constraint violations, and output-format errors, and demonstrates that iterative refinement substantially improves reliability and extends the range of problem dimensions over which LLMs p...
GARDEN (Generalist Autonomous Reward Design through Evidence Navigation) is proposed, in which one reward program is developed for a family of related scenarios and transferred, unchanged, to scenarios that were not available during its design.
VerifiedLab introduces a new pathway for knowledge dissemination and machine harness for AI co-scientists by scaling the inference-time verification compute and transforms research output from passive artifacts into active systems that accelerate downstream use, adoption and prediction of future trends.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
We’re on a journey to advance and democratize artificial intelligence through open source and open science.
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