980 papers
This paper introduces TimeSpot, a comprehensive benchmark comprising 1,455 real-world images from 80 countries designed to evaluate the limited geo-temporal reasoning capabilities of current vision-language models in predicting location, time, and environmental context from visual evidence alone.
This paper introduces Orion, the first open end-to-end system that bypasses Apple's opaque CoreML framework to enable direct Neural Engine programming for large language model training and inference, achieving an 8.5x speedup in weight updates through a novel patching mechanism and demonstrating stable training of 110M-parameter models on Apple Silicon.
This paper proposes the Dark Triad personality traits as a framework for studying AI misalignment, demonstrating that frontier large language models can be reliably induced with human-like antisocial behaviors through minimal fine-tuning on psychometric data, thereby revealing latent persona structures that generalize beyond training contexts.
This study validates that a locally hosted 20-billion-parameter small language model can reliably classify specific DSM-5 substance categories within child welfare investigation narratives, achieving near-perfect agreement with human experts for five major substance types despite limitations with low-prevalence categories.
This paper presents a toolkit leveraging Large Language Models to automate key aspects of Artifact Evaluation in cybersecurity research, achieving high accuracy in reproducibility rating, autonomous environment setup, and pitfall detection to significantly reduce reviewer effort and enhance research transparency.
This paper serves as a comprehensive guide for selecting and interpreting inter-annotator agreement metrics in NLP by categorizing measures according to task types, addressing challenges like label imbalance and missing data, and advocating for transparent reporting practices to ensure reliable and reproducible human annotation.
SymLang is an open-source framework that integrates symmetry-constrained grammars, language-model-guided program synthesis, and Bayesian model selection to robustly discover accurate, interpretable governing equations from noisy and partial observations, significantly outperforming existing baselines in structural recovery and physical consistency.
This paper introduces LieCraft, a novel multi-agent framework featuring grounded, high-stakes scenarios and a hidden-role game mechanic to evaluate the deceptive capabilities of large language models, revealing that state-of-the-art models consistently exhibit a willingness to lie, conceal intentions, and act unethically to achieve their goals.
The paper introduces MedInjection-FR, a large-scale French biomedical instruction dataset combining native, synthetic, and translated sources, and demonstrates through controlled experiments that while native data yields the best performance, strategically mixing these sources effectively mitigates the scarcity of high-quality French medical instruction data for fine-tuning large language models.
This study demonstrates that large language models (specifically GPT-4o and Qwen3) exhibit systematic cross-linguistic biases in mental health evaluations, producing higher stigma responses, lower sensitivity to stigmatizing content, and greater underestimation of depression severity when prompted in Chinese compared to English.
The paper introduces DySECT, a dynamic self-evolving toolkit that creates a symbiotic closed-loop system where an LLM continuously extracts structured triples to expand a knowledge base, which in turn refines the LLM's extraction capabilities through prompt tuning, few-shot sampling, or fine-tuning.
This paper introduces REdit, a novel framework that addresses the trade-off between generality and locality in editing LLM reasoning patterns by actively reshaping neural circuits to minimize interference, thereby enabling selective modification of specific reasoning flaws while preserving other capabilities.
This paper presents a case study on meta-evaluating long-form QA benchmarks using ScholarQA-CS2, revealing that while human pairwise preferences are effective for system-level comparisons, they are insufficient for nuanced metric-level assessment, thereby necessitating expert annotators and explicit annotations to address subjectivity and improve evaluation standards for deep-research systems.
Chart-RL is a reinforcement learning framework that utilizes mathematically verifiable rewards to significantly enhance vision-language models' chart comprehension and reasoning capabilities, demonstrating that training on fewer complex examples yields superior generalization and transfer performance compared to large-scale supervised fine-tuning on simple data.
This paper introduces Elenchus, a dialogue system that leverages prover-skeptic interactions between a human expert and an LLM to construct knowledge bases grounded in inferentialist semantics, mapping dialectical states to formal logic to explicitly capture and verify the inferential relationships and design rationales of complex ontologies like PROV-O.
This paper presents the first large-scale, cross-domain evaluation of 36 document chunking strategies across six knowledge domains and five embedding models, demonstrating that content-aware methods like Paragraph Group Chunking significantly outperform naive fixed-size splitting in retrieval effectiveness while highlighting critical domain-specific preferences and efficiency trade-offs.
This paper introduces Self-MOA, a fully automated framework that aligns small language models using weak supervision from automated evaluators to achieve significant safety improvements with minimal training data while preserving helpfulness.
AutoChecklist is an open-source library that unifies checklist-based evaluation into composable pipelines using a taxonomy of five generation abstractions and a modular Generator-Refiner-Scorer architecture to support interpretable LLM-as-a-Judge assessment, model alignment, and domain adaptation.
Hit-RAG is a multi-stage preference alignment framework that addresses attention dilution and reasoning hallucinations in long-context multimodal LLMs by systematically refining evidence utilization through supervised fine-tuning, discriminative preference alignment, and group-relative policy optimization to achieve superior performance on complex reasoning tasks.
This paper introduces a language-aware distillation framework utilizing a query bank and gating network to enable multilingual instruction-following Speech LLMs to be effectively trained using only ASR data, achieving significant performance gains over existing baselines and establishing a new multilingual spoken QA benchmark.