arxiv arXiv cs.CL · 47d ago · src: 51d ago · research

Factual Retrieval in LLMs Is Non-Contiguous and Redundant

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Large language models use non-contiguous, redundant paths to retrieve factual attributes. These paths often skip layers and involve multiple equivalent routes, indicating distributed and redundant knowledge computation, challenging current understanding of LLM knowledge storage and retrieval.

Importance 2/3 arXiv cs.CL Mistral AI Alibaba (Qwen) Evaluation & benchmarks Reasoning models

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arxiv arXiv cs.AI · 47d ago

LLMs Benchmarked for Web Vulnerability Detection

A study evaluates six LLMs on detecting real-world web vulnerabilities in WordPress plugins, finding detection rates vary by model and prompt design. Claude Opus 4.6 achieved the highest detection rate at 63%, while Qwen 3.5 only reached 35%, and no model consistently identified all baseline vulnerabilities across iterations.

arxiv arXiv cs.AI · 47d ago · 1 view

Benchmark Evaluation of Small Language Models for Arabic NLP

A benchmark of 240 Arabic test items across eight domains and ten skills assesses twelve small language models in zero-shot settings. Gemma 3 (12B) achieved the highest overall score (4.548/5), followed by Aya and C4AI Command Arabic, with performance linked more to Arabic alignment and instruction-following than model size. Common failure modes include prompt leakage, hallucination, and weak task adherence.

arxiv arXiv cs.CL · 47d ago · 6 views

Test-Time Steering Resolves Temporal Fact Conflicts in LLMs

Researchers identify parametric temporal conflicts in language models where outdated facts persist in parameters. They introduce Temporal Attractor Steering (TAS), a test-time method that resolves 29-57% of such conflicts without retraining, maintaining 85-99% accuracy on non-conflict queries and outperforming a baseline on three of four models.

arxiv arXiv cs.CL · 47d ago · 2 views

Benchmark Evaluation of Small Language Models for Arabic NLP

arxiv arXiv cs.LG · 51d ago

De-biased VLM-as-3D-Judge Protocol for Furniture Generation

A de-biased VLM-based judge protocol specializes TRELLIS on furniture generation using lightweight adaptation. The protocol addresses failure modes like image overload and geometry-hiding, with calibration showing 0.83–1.0 win rates and base-vs-base symmetry at 0.5. Among six adaptation methods, conditioner repair under severe degradation achieves parity with the base model, while no method exceeds a 65% win-rate target.