✨Uncertainty-Aware Gradient Signal-to-Noise Data Selection for Instruction Tuning
📝 Summary:
GRADFILTERING is an uncertainty-aware data selection framework for instruction tuning that uses gradient signal-to-noise ratio to improve LLM adaptation efficiency and performance. AI-generated summar...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13697
• PDF: https://arxiv.org/pdf/2601.13697
==================================
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📝 Summary:
GRADFILTERING is an uncertainty-aware data selection framework for instruction tuning that uses gradient signal-to-noise ratio to improve LLM adaptation efficiency and performance. AI-generated summar...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13697
• PDF: https://arxiv.org/pdf/2601.13697
==================================
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❤1
Forwarded from Data Analytics
This repository collects everything you need to use AI and LLM in your projects.
120+ libraries, organized by development stages:
→ Model training, fine-tuning, and evaluation
→ Deploying applications with LLM and RAG
→ Fast and scalable model launch
→ Data extraction, crawlers, and scrapers
→ Creating autonomous LLM agents
→ Prompt optimization and security
Repo: https://github.com/KalyanKS-NLP/llm-engineer-toolkit
🥺 https://t.iss.one/DataAnalyticsX
120+ libraries, organized by development stages:
→ Model training, fine-tuning, and evaluation
→ Deploying applications with LLM and RAG
→ Fast and scalable model launch
→ Data extraction, crawlers, and scrapers
→ Creating autonomous LLM agents
→ Prompt optimization and security
Repo: https://github.com/KalyanKS-NLP/llm-engineer-toolkit
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❤3
✨Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models
📝 Summary:
This survey presents 'Locate, Steer, and Improve' as an actionable framework for mechanistic interpretability in LLMs. It shifts MI from an observational science to a systematic methodology for optimizing LLMs, leading to tangible improvements in their alignment, capability, and efficiency.
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14004
• PDF: https://arxiv.org/pdf/2601.14004
==================================
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#LLM #MechanisticInterpretability #AI #AIAalignment #MachineLearning
📝 Summary:
This survey presents 'Locate, Steer, and Improve' as an actionable framework for mechanistic interpretability in LLMs. It shifts MI from an observational science to a systematic methodology for optimizing LLMs, leading to tangible improvements in their alignment, capability, and efficiency.
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14004
• PDF: https://arxiv.org/pdf/2601.14004
==================================
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#LLM #MechanisticInterpretability #AI #AIAalignment #MachineLearning
✨InT: Self-Proposed Interventions Enable Credit Assignment in LLM Reasoning
📝 Summary:
Intervention Training improves large language model reasoning by enabling fine-grained credit assignment through targeted corrections that localize errors and enhance reinforcement learning performanc...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14209
• PDF: https://arxiv.org/pdf/2601.14209
==================================
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📝 Summary:
Intervention Training improves large language model reasoning by enabling fine-grained credit assignment through targeted corrections that localize errors and enhance reinforcement learning performanc...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14209
• PDF: https://arxiv.org/pdf/2601.14209
==================================
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✨DSAEval: Evaluating Data Science Agents on a Wide Range of Real-World Data Science Problems
📝 Summary:
A comprehensive benchmark for evaluating LLM-based data agents across diverse data science tasks demonstrates superior performance for multimodal agents while highlighting persistent challenges in uns...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13591
• PDF: https://arxiv.org/pdf/2601.13591
==================================
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📝 Summary:
A comprehensive benchmark for evaluating LLM-based data agents across diverse data science tasks demonstrates superior performance for multimodal agents while highlighting persistent challenges in uns...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13591
• PDF: https://arxiv.org/pdf/2601.13591
==================================
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✨RemoteVAR: Autoregressive Visual Modeling for Remote Sensing Change Detection
📝 Summary:
RemoteVAR is a visual autoregressive framework for remote sensing change detection that improves upon existing methods through multi-resolution feature fusion and autoregressive training tailored for ...
🔹 Publication Date: Published on Jan 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11898
• PDF: https://arxiv.org/pdf/2601.11898
• Github: https://github.com/yilmazkorkmaz1/RemoteVAR
==================================
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📝 Summary:
RemoteVAR is a visual autoregressive framework for remote sensing change detection that improves upon existing methods through multi-resolution feature fusion and autoregressive training tailored for ...
🔹 Publication Date: Published on Jan 17
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.11898
• PDF: https://arxiv.org/pdf/2601.11898
• Github: https://github.com/yilmazkorkmaz1/RemoteVAR
==================================
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✨DARC: Decoupled Asymmetric Reasoning Curriculum for LLM Evolution
📝 Summary:
DARC is a two-stage framework stabilizing LLM self-play by decoupling question generation and using asymmetric self-distillation. This mitigates instability and bootstrapping errors, significantly improving reasoning performance across benchmarks without human annotations.
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13761
• PDF: https://arxiv.org/pdf/2601.13761
• Github: https://github.com/RUCBM/DARC
==================================
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📝 Summary:
DARC is a two-stage framework stabilizing LLM self-play by decoupling question generation and using asymmetric self-distillation. This mitigates instability and bootstrapping errors, significantly improving reasoning performance across benchmarks without human annotations.
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13761
• PDF: https://arxiv.org/pdf/2601.13761
• Github: https://github.com/RUCBM/DARC
==================================
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✨Towards Efficient and Robust Linguistic Emotion Diagnosis for Mental Health via Multi-Agent Instruction Refinement
📝 Summary:
APOLO framework uses automated prompt optimization through multi-agent collaboration to improve emotion diagnosis accuracy and robustness in mental healthcare applications. AI-generated summary Lingui...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13481
• PDF: https://arxiv.org/pdf/2601.13481
==================================
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📝 Summary:
APOLO framework uses automated prompt optimization through multi-agent collaboration to improve emotion diagnosis accuracy and robustness in mental healthcare applications. AI-generated summary Lingui...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13481
• PDF: https://arxiv.org/pdf/2601.13481
==================================
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✨Agentic Reasoning for Large Language Models
📝 Summary:
Agentic reasoning redefines LLMs as autonomous agents that plan, act, and learn through continuous interaction in dynamic environments. This survey organizes agentic reasoning by environmental dynamics, from single-agent capabilities to multi-agent collaboration, bridging thought and action throu...
🔹 Publication Date: Published on Jan 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.12538
• PDF: https://arxiv.org/pdf/2601.12538
• Github: https://github.com/weitianxin/Awesome-Agentic-Reasoning
==================================
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📝 Summary:
Agentic reasoning redefines LLMs as autonomous agents that plan, act, and learn through continuous interaction in dynamic environments. This survey organizes agentic reasoning by environmental dynamics, from single-agent capabilities to multi-agent collaboration, bridging thought and action throu...
🔹 Publication Date: Published on Jan 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.12538
• PDF: https://arxiv.org/pdf/2601.12538
• Github: https://github.com/weitianxin/Awesome-Agentic-Reasoning
==================================
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✨Render-of-Thought: Rendering Textual Chain-of-Thought as Images for Visual Latent Reasoning
📝 Summary:
Render-of-Thought framework converts textual reasoning steps into images using vision-language models to improve reasoning traceability and efficiency while maintaining competitive performance. AI-gen...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14750
• PDF: https://arxiv.org/pdf/2601.14750
==================================
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📝 Summary:
Render-of-Thought framework converts textual reasoning steps into images using vision-language models to improve reasoning traceability and efficiency while maintaining competitive performance. AI-gen...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14750
• PDF: https://arxiv.org/pdf/2601.14750
==================================
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✨Lost in the Prompt Order: Revealing the Limitations of Causal Attention in Language Models
📝 Summary:
Research reveals that causal attention in language models creates information bottlenecks when question-answer options follow context, leading to performance drops of over 14 percentage points compare...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14152
• PDF: https://arxiv.org/pdf/2601.14152
==================================
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📝 Summary:
Research reveals that causal attention in language models creates information bottlenecks when question-answer options follow context, leading to performance drops of over 14 percentage points compare...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14152
• PDF: https://arxiv.org/pdf/2601.14152
==================================
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✨Paper2Rebuttal: A Multi-Agent Framework for Transparent Author Response Assistance
📝 Summary:
RebuttalAgent is a multi-agent framework that reframes rebuttal generation as an evidence-centric planning task, improving coverage, faithfulness, and strategic coherence in academic peer review. AI-g...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14171
• PDF: https://arxiv.org/pdf/2601.14171
• Project Page: https://mqleet.github.io/Paper2Rebuttal_ProjectPage/
• Github: https://github.com/AutoLab-SAI-SJTU/Paper2Rebuttal
==================================
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📝 Summary:
RebuttalAgent is a multi-agent framework that reframes rebuttal generation as an evidence-centric planning task, improving coverage, faithfulness, and strategic coherence in academic peer review. AI-g...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14171
• PDF: https://arxiv.org/pdf/2601.14171
• Project Page: https://mqleet.github.io/Paper2Rebuttal_ProjectPage/
• Github: https://github.com/AutoLab-SAI-SJTU/Paper2Rebuttal
==================================
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✨XR: Cross-Modal Agents for Composed Image Retrieval
📝 Summary:
A multi-agent framework for compositional image retrieval that uses specialized agents for generation, filtering, and verification to improve semantic and visual query matching. AI-generated summary R...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14245
• PDF: https://arxiv.org/pdf/2601.14245
• Github: https://01yzzyu.github.io/xr.github.io/
==================================
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📝 Summary:
A multi-agent framework for compositional image retrieval that uses specialized agents for generation, filtering, and verification to improve semantic and visual query matching. AI-generated summary R...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14245
• PDF: https://arxiv.org/pdf/2601.14245
• Github: https://01yzzyu.github.io/xr.github.io/
==================================
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✨Facilitating Proactive and Reactive Guidance for Decision Making on the Web: A Design Probe with WebSeek
📝 Summary:
WebSeek is a mixed-initiative browser extension that enables interactive web data extraction and analysis with AI-assisted guidance and automation. AI-generated summary Web AI agents such as ChatGPT A...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15100
• PDF: https://arxiv.org/pdf/2601.15100
==================================
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📝 Summary:
WebSeek is a mixed-initiative browser extension that enables interactive web data extraction and analysis with AI-assisted guidance and automation. AI-generated summary Web AI agents such as ChatGPT A...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15100
• PDF: https://arxiv.org/pdf/2601.15100
==================================
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✨Rethinking Video Generation Model for the Embodied World
📝 Summary:
A comprehensive robotics benchmark evaluates video generation models across multiple task domains and embodiments, revealing deficiencies in physical realism and introducing a large-scale dataset to a...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15282
• PDF: https://arxiv.org/pdf/2601.15282
• Project Page: https://dagroup-pku.github.io/ReVidgen.github.io/
• Github: https://github.com/DAGroup-PKU/ReVidgen/
✨ Datasets citing this paper:
• https://huggingface.co/datasets/DAGroup-PKU/RBench
• https://huggingface.co/datasets/DAGroup-PKU/RoVid-X
✨ Spaces citing this paper:
• https://huggingface.co/spaces/DAGroup-PKU/RBench-Leaderboard
==================================
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📝 Summary:
A comprehensive robotics benchmark evaluates video generation models across multiple task domains and embodiments, revealing deficiencies in physical realism and introducing a large-scale dataset to a...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15282
• PDF: https://arxiv.org/pdf/2601.15282
• Project Page: https://dagroup-pku.github.io/ReVidgen.github.io/
• Github: https://github.com/DAGroup-PKU/ReVidgen/
✨ Datasets citing this paper:
• https://huggingface.co/datasets/DAGroup-PKU/RBench
• https://huggingface.co/datasets/DAGroup-PKU/RoVid-X
✨ Spaces citing this paper:
• https://huggingface.co/spaces/DAGroup-PKU/RBench-Leaderboard
==================================
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✨FARE: Fast-Slow Agentic Robotic Exploration
📝 Summary:
FARE is a hierarchical exploration framework that combines large language model reasoning with reinforcement learning control to enable efficient autonomous robot navigation in complex environments. A...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14681
• PDF: https://arxiv.org/pdf/2601.14681
==================================
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📝 Summary:
FARE is a hierarchical exploration framework that combines large language model reasoning with reinforcement learning control to enable efficient autonomous robot navigation in complex environments. A...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14681
• PDF: https://arxiv.org/pdf/2601.14681
==================================
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✨RoboBrain 2.5: Depth in Sight, Time in Mind
📝 Summary:
RoboBrain 2.5 enhances embodied AI through improved 3D spatial reasoning and temporal value estimation for more precise manipulation tasks. AI-generated summary We introduce RoboBrain 2.5, a next-gene...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14352
• PDF: https://arxiv.org/pdf/2601.14352
• Project Page: https://superrobobrain.github.io/
==================================
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📝 Summary:
RoboBrain 2.5 enhances embodied AI through improved 3D spatial reasoning and temporal value estimation for more precise manipulation tasks. AI-generated summary We introduce RoboBrain 2.5, a next-gene...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.14352
• PDF: https://arxiv.org/pdf/2601.14352
• Project Page: https://superrobobrain.github.io/
==================================
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✨MMDeepResearch-Bench: A Benchmark for Multimodal Deep Research Agents
📝 Summary:
MMDeepResearch-Bench evaluates multimodal research agents on report generation with visual evidence, revealing trade-offs between prose quality, citation accuracy, and visual grounding. AI-generated s...
🔹 Publication Date: Published on Jan 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.12346
• PDF: https://arxiv.org/pdf/2601.12346
• Github: https://github.com/AIoT-MLSys-Lab/MMDeepResearch-Bench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/MMDR-2025/MMdeepresearch
==================================
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📝 Summary:
MMDeepResearch-Bench evaluates multimodal research agents on report generation with visual evidence, revealing trade-offs between prose quality, citation accuracy, and visual grounding. AI-generated s...
🔹 Publication Date: Published on Jan 18
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.12346
• PDF: https://arxiv.org/pdf/2601.12346
• Github: https://github.com/AIoT-MLSys-Lab/MMDeepResearch-Bench
✨ Datasets citing this paper:
• https://huggingface.co/datasets/MMDR-2025/MMdeepresearch
==================================
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✨FinVault: Benchmarking Financial Agent Safety in Execution-Grounded Environments
📝 Summary:
FinVault presents the first execution-grounded security benchmark for financial agents, revealing significant vulnerabilities in current defense mechanisms when applied to real-world financial workflo...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07853
• PDF: https://arxiv.org/pdf/2601.07853
• Github: https://github.com/aifinlab/FinVault
==================================
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📝 Summary:
FinVault presents the first execution-grounded security benchmark for financial agents, revealing significant vulnerabilities in current defense mechanisms when applied to real-world financial workflo...
🔹 Publication Date: Published on Jan 9
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.07853
• PDF: https://arxiv.org/pdf/2601.07853
• Github: https://github.com/aifinlab/FinVault
==================================
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✨The Responsibility Vacuum: Organizational Failure in Scaled Agent Systems
📝 Summary:
Modern CI/CD pipelines integrating agent-generated code exhibit a structural failure in responsibility attribution. Decisions are executed through formally correct approval processes, yet no entity po...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15059
• PDF: https://arxiv.org/pdf/2601.15059
==================================
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📝 Summary:
Modern CI/CD pipelines integrating agent-generated code exhibit a structural failure in responsibility attribution. Decisions are executed through formally correct approval processes, yet no entity po...
🔹 Publication Date: Published on Jan 21
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.15059
• PDF: https://arxiv.org/pdf/2601.15059
==================================
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✨AgentEHR: Advancing Autonomous Clinical Decision-Making via Retrospective Summarization
📝 Summary:
AgentEHR is a benchmark for autonomous EHR navigation involving complex clinical decision-making in raw data. The RetroSum framework addresses information loss and fractured reasoning through retrospective summarization and evolving experience strategies. RetroSum improves performance by up to 29...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13918
• PDF: https://arxiv.org/pdf/2601.13918
• Github: https://github.com/BlueZeros/AgentEHR
==================================
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📝 Summary:
AgentEHR is a benchmark for autonomous EHR navigation involving complex clinical decision-making in raw data. The RetroSum framework addresses information loss and fractured reasoning through retrospective summarization and evolving experience strategies. RetroSum improves performance by up to 29...
🔹 Publication Date: Published on Jan 20
🔹 Paper Links:
• arXiv Page: https://arxiv.org/abs/2601.13918
• PDF: https://arxiv.org/pdf/2601.13918
• Github: https://github.com/BlueZeros/AgentEHR
==================================
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