New benchmark DRBench and four-stage supervision framework DRScaffold improve dense-scene reasoning in lightweight VLMs, with a 3B model surpassing a frozen 32B model on the benchmark while maintaining general performance.
Deconstructing long chain-of- thought: A structured reasoning optimization framework for long cot distillation.arXiv preprint arXiv:2503.16385
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LONSREX introduces a metric-based pipeline to identify necessary and sufficient rationales when creating training data for fine-tuning LLMs on explainable misinformation detection, addressing limitations of naive label-based filtering.
CoRD uses collaborative multi-teacher step-wise decoding with perplexity-guided beam search to generate higher-quality Long-CoT data that lets smaller models reach near-teacher performance with less supervision.
Average log probability selection for LLM reasoning datasets is confounded by step length because longer steps dilute low-probability first tokens; ASLEC-DROP and ASLEC-CASL remove this bias.
Training-Trajectory-Aware Token Selection (T3S) reconstructs the token-level training objective to overcome a performance bottleneck in continual distillation of reasoning capabilities from large to small language models.
GPlan compresses LLM reasoning into small models via Progressive Implicit CoT Distillation and Spatiotemporal Counterfactual DPO to generate logically coherent and physically executable intent sequences for recommendation.
TRUST is a decentralized AI auditing framework that decomposes reasoning into HDAGs, maps agent interactions via the DAAN protocol to CIGs, and uses stake-weighted multi-tier consensus to achieve 72.4% accuracy while proving a Safety-Profitability Theorem that rewards honest auditors.
Reasoning LLMs aggregate social biases through stereotype repetition and irrelevant information injection in their thinking processes, and a self-review prompt mitigates this on BBQ, StereoSet, and BOLD benchmarks.
citing papers explorer
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DRScaffold: Boosting Dense-Scene Reasoning in Lightweight Vision Language Models
New benchmark DRBench and four-stage supervision framework DRScaffold improve dense-scene reasoning in lightweight VLMs, with a 3B model surpassing a frozen 32B model on the benchmark while maintaining general performance.
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Are Rationales Necessary and Sufficient? Tuning LLMs for Explainable Misinformation Detection
LONSREX introduces a metric-based pipeline to identify necessary and sufficient rationales when creating training data for fine-tuning LLMs on explainable misinformation detection, addressing limitations of naive label-based filtering.
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Distilling Long-CoT Reasoning through Collaborative Step-wise Multi-Teacher Decoding
CoRD uses collaborative multi-teacher step-wise decoding with perplexity-guided beam search to generate higher-quality Long-CoT data that lets smaller models reach near-teacher performance with less supervision.
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On the Step Length Confounding in LLM Reasoning Data Selection
Average log probability selection for LLM reasoning datasets is confounded by step length because longer steps dilute low-probability first tokens; ASLEC-DROP and ASLEC-CASL remove this bias.
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Training-Trajectory-Aware Token Selection
Training-Trajectory-Aware Token Selection (T3S) reconstructs the token-level training objective to overcome a performance bottleneck in continual distillation of reasoning capabilities from large to small language models.
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Generative Spatiotemporal Intent Sequence Recommendation via Implicit Reasoning in Amap
GPlan compresses LLM reasoning into small models via Progressive Implicit CoT Distillation and Spatiotemporal Counterfactual DPO to generate logically coherent and physically executable intent sequences for recommendation.
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TRUST: A Framework for Decentralized AI Service v.0.1
TRUST is a decentralized AI auditing framework that decomposes reasoning into HDAGs, maps agent interactions via the DAAN protocol to CIGs, and uses stake-weighted multi-tier consensus to achieve 72.4% accuracy while proving a Safety-Profitability Theorem that rewards honest auditors.
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Investigating Thinking Behaviours of Reasoning-Based Language Models for Social Bias Mitigation
Reasoning LLMs aggregate social biases through stereotype repetition and irrelevant information injection in their thinking processes, and a self-review prompt mitigates this on BBQ, StereoSet, and BOLD benchmarks.