Who's Who of AI

AK

495 trust @_akhaliq · 516,758 followers
Why they matter

Tracked through public AI activity and peer connections inside the directory.

AI signals
11
past 30d
Sources
2
distinct domains
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0
past 30d
Latest signal
9d ago
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What they're sharing

Articles & links

paper: https://t.co/gD6tgeLIOt

Paper page - Orca: The World is in Your Mind huggingface.co
AI Weekly's analysis →
  • Orca pretrains on 125K hours of video and 160M event annotations using a single Next-State-Prediction objective on a frozen Qwen3.5 backbone.
  • Orca-4B averages 51.8 across MVBench, TemporalBench, 3DSRBench, and SWITCH, ahead of Qwen3.5-4B's 46.7 at the same size.
  • On a real-robot out-of-distribution test Orca reports 36.6% versus π₀.5's 27.6%, despite using no action labels in pre-training.
Read full analysis →
View on Bluesky · ♥ 0 ↻ 0 ↩ 0 · 3 from the directory shared this · 64d ago

SWE-Bench ProMax Benchmarking Agents on Large-Scale Multilingual Code Refactoring paper: https://t.co/2DO56pDC95 https://t.co/JtZpMSoTNF

Paper page - SWE-Bench ProMax: Benchmarking Agents on Large-Scale Multilingual Code Refactoring huggingface.co
AI Weekly's analysis →
  • SWE-Bench ProMax curates 170 refactoring tasks across seven languages, averaging 11.4 modified files and 261.6 lines of code per instance.
  • GPT-5.2 leads at 41.2% resolve rate under the OpenHands scaffold, far below the 75%+ that top agents post on SWE-bench Verified.
  • Open-weight GLM-5 hits 36.5% at $0.24 per instance, roughly one-twentieth the cost of Claude Sonnet 4.6's 38.8% at $4.77.
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View on Bluesky · ♥ 0 ↻ 0 ↩ 0 · 24d ago

Toward Skill-Native LLMs Skill Entropy for Benchmarking and Training Long-Horizon Reasoning paper: https://t.co/3WsZbeXLIH https://t.co/9oieS0C4U9

Paper page - Toward Skill-Native LLMs: Skill Entropy for Benchmarking and Training Long-Horizon Reasoning huggingface.co
AI Weekly's analysis →
  • Princeton-led team introduces Skill Entropy, a pairwise measure of how hard it is to switch between reasoning skills inside one chain of reasoning.
  • Skill2-Bench spans 558 skills across 9 domains; frontier models lose 4 to 13 points when the same skill runs inside a cross-skill task.
  • Skill-Entropy RL lifts Qwen3-4B-Instruct on Skill2-Bench from 34.4% to 68.4%, and Qwen3-1.7B from 14.6% to 40.1%.
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View on Bluesky · ♥ 0 ↻ 0 ↩ 0 · 28d ago

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