You didn't understand either. Yes, the independence of errors is an assumption, which may or may not be reasonable. No, errors are NOT RECOVERABLE in an auto-regressive setting because the set of correct answers form a subtree in the tree of all possible sequences. Once you get
LLMS
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Autoregressive Prediction and Stochastic Generation in AI Models
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No. Auto-regressive prediction produces a single path in the tree of possible sequences. At non-zero temperature (stochastic generation) the potential paths form a subtree of the full tree, or rather, a distribution over all paths in the tree. If you threshold all the paths
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Using Codex Locally for Better Codebase Analysis Results
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use codex locally on the codebase, will give you better results.
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Test-Time Adaptation via Many-Shot Prompting: Key Research Findings
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Another day, another paper from our team 😎 Key findings from "Test-Time Adaptation via Many-Shot Prompting": Many-shot helps structured tasks, hurts open-ended generation Performance hinges on example selection, not quantity Dynamic ICL beats brute-force context expansion 🔗arxiv.org/abs/2603.05829?utm…
→ View original post on X — @sambanovaai, 2026-03-31 20:00 UTC
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Context Window Limitations in AI Models Explained
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if you go beyond the 256k context window it falls off quite a bit yeah, but default limits that.
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Claude’s rapid evolution toward autonomous agent capabilities
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They've released a number of features already heavily hinting on nudging Code into Claw directions, i.e. it's a speedrun of
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LLMs as CPUs: Statistical Processing and Agent Operating Systems
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LLM = CPU (data: tokens not bytes, dynamics: statistical and vague not deterministic and precise)
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Terminal-Bench 3.0 and the Benchmark Factory Revolution
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Terminal-Bench 2.0 went from ~25% → 80% in four months and became the standard eval for frontier CLI agents. Now, TB3 is in the works.
— vincent sunn chen (@vincentsunnchen) 31 mars 2026
I talked to @alexgshaw about what happens when model capabilities climb faster than we can measure them.
His answer: the benchmark factory… pic.twitter.com/phNP7ni43tTerminal-Bench 2.0 went from ~25% → 80% in four months and became the standard eval for frontier CLI agents. Now, TB3 is in the works. I talked to @alexgshaw about what happens when model capabilities climb faster than we can measure them. His answer: the benchmark factory (@harborframework)— infrastructure to develop hard, representative evals at the pace that the frontier moves. As Alex put it: "we need a thousand times more benchmarks than we have right now." 00:23 – How quickly models hill-climbed TB2 01:46 – What rapid progress reveals about benchmarks vs. real-world capability 03:28 – What made Terminal-Bench stick 04:58 – Why the terminal is the right abstraction for agentic AI 07:14 – How TB2 maintains task quality at scale 09:23 – Managing benchmark integrity in a benchmaxxing world 10:47 – Harbor: from experiment to benchmark factory 12:19 – What Harbor does that nothing else did 14:37 – The invariants: what won't change as agent evals evolve 16:55 – The benchmark Alex most wants to see built 18:18 – The ideal human-in-the-loop task creation flywheel 20:32 – How to contribute to Terminal-Bench 3.0
→ View original post on X — @snorkelai, 2026-03-31 18:50 UTC
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LangChain Posts Humorous Ad About Token Spending
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Great ad by @LangChain "token spend higher than your rent?" [Translated from EN to English]
→ View original post on X — @langchain, 2026-03-31 18:10 UTC
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OpenAI’s Codex Confirmed Open Source Since Inception
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lol for the avoidance of any doubt, the repo has been open source since it's inception! this was a post essentially bringing visibility to that fact that codex is open source source: I work on codex at openai