-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
Collections
Discover the best community collections!
Collections including paper arxiv:2509.19284
-
What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
Paper • 2509.19284 • Published • 22 -
Soft Tokens, Hard Truths
Paper • 2509.19170 • Published • 15 -
CompLLM: Compression for Long Context Q&A
Paper • 2509.19228 • Published • 8 -
Test-Time Scaling in Reasoning Models Is Not Effective for Knowledge-Intensive Tasks Yet
Paper • 2509.06861 • Published • 8
-
Open Data Synthesis For Deep Research
Paper • 2509.00375 • Published • 70 -
Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training
Paper • 2509.03403 • Published • 22 -
LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations
Paper • 2509.03405 • Published • 23 -
SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs
Paper • 2509.00930 • Published • 4
-
Large Reasoning Models Learn Better Alignment from Flawed Thinking
Paper • 2510.00938 • Published • 58 -
What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
Paper • 2509.19284 • Published • 22 -
Learning to Reason as Action Abstractions with Scalable Mid-Training RL
Paper • 2509.25810 • Published • 5 -
Agent Learning via Early Experience
Paper • 2510.08558 • Published • 266
-
A Tale of Tails: Model Collapse as a Change of Scaling Laws
Paper • 2402.07043 • Published • 16 -
What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
Paper • 2509.19284 • Published • 22 -
OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System
Paper • 2509.18091 • Published • 33 -
Strategic Dishonesty Can Undermine AI Safety Evaluations of Frontier LLM
Paper • 2509.18058 • Published • 12
-
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 18 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48
-
Can Large Language Models Understand Context?
Paper • 2402.00858 • Published • 23 -
OLMo: Accelerating the Science of Language Models
Paper • 2402.00838 • Published • 85 -
Self-Rewarding Language Models
Paper • 2401.10020 • Published • 151 -
SemScore: Automated Evaluation of Instruction-Tuned LLMs based on Semantic Textual Similarity
Paper • 2401.17072 • Published • 25
-
Large Reasoning Models Learn Better Alignment from Flawed Thinking
Paper • 2510.00938 • Published • 58 -
What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
Paper • 2509.19284 • Published • 22 -
Learning to Reason as Action Abstractions with Scalable Mid-Training RL
Paper • 2509.25810 • Published • 5 -
Agent Learning via Early Experience
Paper • 2510.08558 • Published • 266
-
What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
Paper • 2509.19284 • Published • 22 -
Soft Tokens, Hard Truths
Paper • 2509.19170 • Published • 15 -
CompLLM: Compression for Long Context Q&A
Paper • 2509.19228 • Published • 8 -
Test-Time Scaling in Reasoning Models Is Not Effective for Knowledge-Intensive Tasks Yet
Paper • 2509.06861 • Published • 8
-
A Tale of Tails: Model Collapse as a Change of Scaling Laws
Paper • 2402.07043 • Published • 16 -
What Characterizes Effective Reasoning? Revisiting Length, Review, and Structure of CoT
Paper • 2509.19284 • Published • 22 -
OnePiece: Bringing Context Engineering and Reasoning to Industrial Cascade Ranking System
Paper • 2509.18091 • Published • 33 -
Strategic Dishonesty Can Undermine AI Safety Evaluations of Frontier LLM
Paper • 2509.18058 • Published • 12
-
Open Data Synthesis For Deep Research
Paper • 2509.00375 • Published • 70 -
Beyond Correctness: Harmonizing Process and Outcome Rewards through RL Training
Paper • 2509.03403 • Published • 22 -
LMEnt: A Suite for Analyzing Knowledge in Language Models from Pretraining Data to Representations
Paper • 2509.03405 • Published • 23 -
SATQuest: A Verifier for Logical Reasoning Evaluation and Reinforcement Fine-Tuning of LLMs
Paper • 2509.00930 • Published • 4
-
Describe What You See with Multimodal Large Language Models to Enhance Video Recommendations
Paper • 2508.09789 • Published • 5 -
MM-BrowseComp: A Comprehensive Benchmark for Multimodal Browsing Agents
Paper • 2508.13186 • Published • 18 -
ZARA: Zero-shot Motion Time-Series Analysis via Knowledge and Retrieval Driven LLM Agents
Paper • 2508.04038 • Published • 1 -
Prompt Orchestration Markup Language
Paper • 2508.13948 • Published • 48