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Overview
Research papers and engineering breakthroughs, distilled for engineers who build. StackSweep cuts every paper to the number that matters.
News
Five Hypotheses for Why LLMs Fail at Tabular Data.
A systematic study rules out four plausible explanations for why LLMs underperform classical ML on tabular classification, and finds the real culprit: accuracy degrades with feature count in a way no noise-corrupted classical model reproduces, and the mod
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How GPT-Live Kills the Turn Detector: A System Design Teardown
OpenAI's GPT-Live writeup is a real systems engineering case study: full-duplex audio, hot model handoffs, a 6-to-1 round-trip protocol, and capacity planning that isn't about GPU throughput. We break down the five design patterns worth studying, with a q
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What Actually Keeps an AI Benchmark Useful? Scale
A systematic study of 60 LLM benchmarks finds 29 have saturated: top models are statistically indistinguishable. Age and test set size predict saturation; private test sets, multilingual scope, and open-ended formats don't protect against it once age is c
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The Hidden Tax of Messy Code: More Tokens, More Backtracking, Same Result
A SonarSource controlled study finds code cleanliness doesn't change whether coding agents pass a task, but messy code makes them burn 8% more tokens and re-open edited files 34% more often. The quality tax is efficiency, not success.
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Same Weights, Better Agent: Teaching Models to Tune Their Own Harness
Self-Harness lets an agent improve its own scaffolding, no stronger model or human engineer required. By mining its failure traces and regression-testing edits, MiniMax M2.5 jumped from 40.5% to 61.9% on Terminal-Bench-2.0 with frozen weights.
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