ACON Optimizing Context Compression for Long-horizon LLM Agents
@_akhaliq
-
Knapsack RL: Budget Allocation for LLM Exploration
By
–
Knapsack RL Unlocking Exploration of LLMs via Optimizing Budget Allocation
-
DeepSearch: Reinforcement Learning with Verifiable Rewards via MCTS
By
–
DeepSearch Overcome the Bottleneck of Reinforcement Learning with Verifiable Rewards via Monte Carlo Tree Search
-
Wan2.2-T2V-A14B-4steps-lora-250928 Model Release
By
–
Wan2.2-T2V-A14B-4steps-lora-250928 pic.twitter.com/5vZJqNlEFQ
— AK (@_akhaliq) 30 septembre 2025Wan2.2-T2V-A14B-4steps-lora-250928
-
PromptCoT 2.0: Scaling Prompt Synthesis for LLM Reasoning
By
–
PromptCoT 2.0 Scaling Prompt Synthesis for Large Language Model Reasoning
-
AnyCoder now available on Hugging Face Spaces
By
–
Available in anycoder: https://
huggingface.co/spaces/akhaliq
/anycoder
… -
LongLive: Real-time Interactive Long Video Generation Technology
By
–
LongLive
— AK (@_akhaliq) 29 septembre 2025
Real-time Interactive Long Video Generation pic.twitter.com/oSPtiqZoGWLongLive Real-time Interactive Long Generation
-
Anycoder Now Default Feature with Vibe Coded App
By
–
now default in anycoder: https://
huggingface.co/spaces/akhaliq
/anycoder
… and vibe coded an app with it -
KAT-Dev-32B: 32B Model Achieves 62.4% on SWE-Bench Verified
By
–
KAT-Dev-32B 32B-parameter model for software engineering tasks On SWE-Bench Verified, KAT-Dev-32B achieves comparable performance with 62.4% resolved and ranks 5th among all open-source models with different scales vibe coded a quick chat app in anycoder
