WhiFlash introduces token-level cross-paradigm routing between autoregressive and diffusion drafting models, with cache optimizations, to raise acceptance lengths and deliver up to 69.6% throughput gains over EAGLE-3.
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An agentic weak–strong Self-Instruct loop, optionally meta-optimized, produces synthetic data that trains small models better than standard CoT Self-Instruct across three domains.
A 9B multimodal model learns to tailor raw video/GUI streams into schema-aligned training data, matching a proprietary annotator on downstream tasks; the abstract's capacity-scaling claims are not supported by the body.
WRIT is a synthesis pipeline that generates write-read intensive trajectories along axes of write-decision count and per-decision evidence burden, enabling a 4B model to outperform GPT-5.1 on τ²-bench with reduced inference tokens.
SkillGen synthesizes auditable skills from agent trajectories via contrastive induction on successes and failures, then verifies net performance impact by comparing outcomes with and without the skill on identical tasks.
RL training compute for logical reasoning follows a power law with horizon depth whose exponent rises with logical expressiveness, yielding better downstream transfer when models train on richer logics.
TREX automates the LLM training lifecycle via collaborative agents and tree-based exploration, delivering consistent performance gains across 10 real-world fine-tuning tasks in FT-Bench.
Execution-grounded multi-turn OS trajectories from 4D intents and a role-locked simulator lift Qwen3-8B ClawEval pass@1 from 19.3 to 37.7, beating GPT-4o and Qwen3-32B zero-shot.
Controlled experiments on synthetic post-training data show provenance-grounded gating and adaptive recovery improve yield and recall over baselines, with generator scale as the primary driver of downstream fine-tuning quality.
Proposes image-bank harness and ODE closed-loop data generation to boost multimodal deep search agents, reporting average score gains from 24.9% to 39.0% on 8 benchmarks for 8B model and 30.6% to 41.5% for 30B.
Kimi K2 is a 1-trillion-parameter MoE model that leads open-source non-thinking models on agentic benchmarks including 65.8 on SWE-Bench Verified and 66.1 on Tau2-Bench.
Claw-R1 provides a Gateway Server and Data Pool to manage step-level agent interaction traces as structured data assets for agentic RL training.
A 14B reasoning model trained via supervised fine-tuning on selected prompts and o3-mini traces, plus outcome RL, outperforms larger open models like DeepSeek-R1-Distill-Llama-70B on math, coding, planning and related benchmarks.
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.
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citing papers explorer
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WhiFlash: Accelerating Speculative Decoding with Token-Level Cross-Paradigm Routing
WhiFlash introduces token-level cross-paradigm routing between autoregressive and diffusion drafting models, with cache optimizations, to raise acceptance lengths and deliver up to 69.6% throughput gains over EAGLE-3.
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Autodata: An agentic data scientist to create high quality synthetic data
An agentic weak–strong Self-Instruct loop, optionally meta-optimized, produces synthetic data that trains small models better than standard CoT Self-Instruct across three domains.
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DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams
A 9B multimodal model learns to tailor raw video/GUI streams into schema-aligned training data, matching a proprietary annotator on downstream tasks; the abstract's capacity-scaling claims are not supported by the body.
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WRIT: Write-Read Intensive Trajectory Synthesis for Multi-Turn User-Facing Agents
WRIT is a synthesis pipeline that generates write-read intensive trajectories along axes of write-decision count and per-decision evidence burden, enabling a 4B model to outperform GPT-5.1 on τ²-bench with reduced inference tokens.
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SkillGen: Verified Inference-Time Agent Skill Synthesis
SkillGen synthesizes auditable skills from agent trajectories via contrastive induction on successes and failures, then verifies net performance impact by comparing outcomes with and without the skill on identical tasks.
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Can RL Teach Long-Horizon Reasoning to LLMs? Expressiveness Is Key
RL training compute for logical reasoning follows a power law with horizon depth whose exponent rises with logical expressiveness, yielding better downstream transfer when models train on richer logics.
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TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration
TREX automates the LLM training lifecycle via collaborative agents and tree-based exploration, delivering consistent performance gains across 10 real-world fine-tuning tasks in FT-Bench.
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ISE: An Execution-Grounded Recipe for Multi-Turn OS-Agent Trajectories
Execution-grounded multi-turn OS trajectories from 4D intents and a role-locked simulator lift Qwen3-8B ClawEval pass@1 from 19.3 to 37.7, beating GPT-4o and Qwen3-32B zero-shot.
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Provenance-Grounded Gating and Adaptive Recovery in Synthetic Post-Training Data Curation
Controlled experiments on synthetic post-training data show provenance-grounded gating and adaptive recovery improve yield and recall over baselines, with generator scale as the primary driver of downstream fine-tuning quality.
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Towards On-Policy Data Evolution for Visual-Native Multimodal Deep Search Agents
Proposes image-bank harness and ODE closed-loop data generation to boost multimodal deep search agents, reporting average score gains from 24.9% to 39.0% on 8 benchmarks for 8B model and 30.6% to 41.5% for 30B.
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Kimi K2: Open Agentic Intelligence
Kimi K2 is a 1-trillion-parameter MoE model that leads open-source non-thinking models on agentic benchmarks including 65.8 on SWE-Bench Verified and 66.1 on Tau2-Bench.
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Claw-R1: A Step-Level Data Middleware System for Agentic Reinforcement Learning
Claw-R1 provides a Gateway Server and Data Pool to manage step-level agent interaction traces as structured data assets for agentic RL training.
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Phi-4-reasoning Technical Report
A 14B reasoning model trained via supervised fine-tuning on selected prompts and o3-mini traces, plus outcome RL, outperforms larger open models like DeepSeek-R1-Distill-Llama-70B on math, coding, planning and related benchmarks.
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From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review
A survey consolidating benchmarks, agent frameworks, real-world applications, and protocols for LLM-based autonomous agents into a proposed taxonomy with recommendations for future research.
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Multi-Agent Collaboration Mechanisms: A Survey of LLMs
The survey organizes LLM-based multi-agent collaboration mechanisms into a framework with dimensions of actors, types, structures, strategies, and coordination protocols, reviews applications across domains, and identifies challenges for future research.