deadbydawn101/RavenX-CyberAgent-Qwen3.6-35B-A3B-Opus-4.7-OpenMythos-Pentester-BugHunter-RATH-mlx
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E.M. Rabani PRO
SkyMind
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Neuromorphic NNs
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TropicalGT and ToricGT: Tropical, Toric, and Graph-Token Geometry for Auditable Reasoning
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reacted to black-yt's post with 🔥 10 days ago
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Hey all — our ResearchClawBench leaderboard just updated 🔥
We let AI do real science: 40 tasks across 10 disciplines, compared to human papers. Hard example? 🏔️ Glacier mass change — AI must integrate 233 datasets from 35 teams, 4 methods, reproduce 6542±387 Gt ice loss vs IPCC. No toy problems.
Latest leaderboard (2026-06-09) 📊:
Agents: 🥇 Claude Code 21.5 (50 = match human), $5.3; 🥈 EvoScientist 18.8, $4.1; 🥉 Codex CLI 18.4, just $2.0
LLMs+Harness: 🥇 Claude-Opus-4.8 21.1, $4.0; 🥈 Claude-Opus-4.7 20.7; 🥉 MiniMax-M3 19.8, only $0.45; Qwen3.7-Max 18.7, $0.42, 11min 💥
Claude still king, but MiniMax/Qwen/DeepSeek are crazy cheap and competitive. Expensive isn't always better.
📎 Code & star: https://github.com/InternScience/ResearchClawBench
🏠 Website: https://internscience.github.io/ResearchClawBench-Home/
🤗 Upvote paper: ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research (2606.07591)
We let AI do real science: 40 tasks across 10 disciplines, compared to human papers. Hard example? 🏔️ Glacier mass change — AI must integrate 233 datasets from 35 teams, 4 methods, reproduce 6542±387 Gt ice loss vs IPCC. No toy problems.
Latest leaderboard (2026-06-09) 📊:
Agents: 🥇 Claude Code 21.5 (50 = match human), $5.3; 🥈 EvoScientist 18.8, $4.1; 🥉 Codex CLI 18.4, just $2.0
LLMs+Harness: 🥇 Claude-Opus-4.8 21.1, $4.0; 🥈 Claude-Opus-4.7 20.7; 🥉 MiniMax-M3 19.8, only $0.45; Qwen3.7-Max 18.7, $0.42, 11min 💥
Claude still king, but MiniMax/Qwen/DeepSeek are crazy cheap and competitive. Expensive isn't always better.
📎 Code & star: https://github.com/InternScience/ResearchClawBench
🏠 Website: https://internscience.github.io/ResearchClawBench-Home/
🤗 Upvote paper: ResearchClawBench: A Benchmark for End-to-End Autonomous Scientific Research (2606.07591)
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reacted to RiverRider's post with 🚀 10 days ago
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ATTENTION: The SRT-Introspect framework moves past surface-level output commentary by supplying real-time natural language interpretations of a model’s latent states. These verbalizations are validated, not merely asserted, through a round-trip reconstruction procedure. Natural language descriptions derived from hidden activations are passed through an encoder that reconstructs the corresponding activation vector; the recovered vector closely approximates the original. High reconstruction fidelity indicates that the verbalizations encode genuine structural information about the internal state rather than offering plausible but ungrounded speculation.
This validated introspection converts what has often remained a theoretical or post-hoc exercise into a practical instrument for auditing model behavior, diagnosing failure modes, and providing high-level semantic guidance—all without modifying the base model or incurring the costs of fine-tuning. Because the mechanism operates on frozen configurations, it can be applied to production systems where any change to weights or architecture is undesirable. Thank you for your attention.
Run a trace: RiverRider/srt-introspect
Repo: https://github.com/space-bacon/SRT
This validated introspection converts what has often remained a theoretical or post-hoc exercise into a practical instrument for auditing model behavior, diagnosing failure modes, and providing high-level semantic guidance—all without modifying the base model or incurring the costs of fine-tuning. Because the mechanism operates on frozen configurations, it can be applied to production systems where any change to weights or architecture is undesirable. Thank you for your attention.
Run a trace: RiverRider/srt-introspect
Repo: https://github.com/space-bacon/SRT
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This is not a pipe.
Everyone is born a semiotician, no one is born knowing it. Go easy on yourself (and me) for not understanding this yet.
Computational semiotics is now an empirical study.
LLMs are not proto-minds. They are verifiably semiotic infrastructure.
This repository (or attached demo) can show you, in real time, how any frozen model (Qwen for demo) arrives at any answer by reading its latent states directly during generation.
Any questions?
RiverRider/srt-introspect
Repo:
https://github.com/space-bacon/SRT
Grok insist my intro is condescending … This is certainly true, as is the statement in my condescended opinion. I expect heat for it, let’s think this through?
Everyone is born a semiotician, no one is born knowing it. Go easy on yourself (and me) for not understanding this yet.
Computational semiotics is now an empirical study.
LLMs are not proto-minds. They are verifiably semiotic infrastructure.
This repository (or attached demo) can show you, in real time, how any frozen model (Qwen for demo) arrives at any answer by reading its latent states directly during generation.
Any questions?
RiverRider/srt-introspect
Repo:
https://github.com/space-bacon/SRT
Grok insist my intro is condescending … This is certainly true, as is the statement in my condescended opinion. I expect heat for it, let’s think this through?
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