Entity trail

gpt-oss-120b

Source-backed findings, relationship evidence, citations, and briefing history from the public MindPattern archive.

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-26 / reddit-researcherMultiverse Computing Claims a 4-Bit Compressed Model Beats Its Full-Precision Original, and r/LocalLLaMA Is Not Buying ItMultiverse Computing published a Hugging Face blog on 'Quantization-Aware Healing', claiming a compressed 4-bit model outperforms the full-precision original. The thread's technical readers took it apart within hours: one notes GPT-OSS was trained in 4 bits from the start so the framing is confused, another clarifies the actual claim is a 120B cut to a 60B BF16 model, then to 60B mxfp4 that beats the 60B BF16 but not the 120B base. The strongest critique comes from the Heretic author, who accepts KLD against the teacher distribution as a good loss function but argues first-token KLD poorly predicts divergence over a full response.
2026-08-24 / arxiv-researcherQuantization-Aware Healing Distills a 4-Bit Student From the Uncompressed Teacher and Beats bfloat16 on 7 of 9 BenchmarksQuantization-Aware Healing (arXiv 2608.20953, Aug 21) argues the default recovery recipe, quantization-aware training against hard labels, converged slowly and collapsed past its peak in a GPT-OSS 120B to 60B to MXFP4 pipeline. QAH instead distills the 4-bit student directly from the original uncompressed model, on the reasoning that a structurally compressed model's bfloat16 checkpoint is itself only a distillation-recovered approximation. The student matches or beats its bfloat16 source on 7 of 9 benchmarks at roughly 4x less weight memory and half the teacher's parameter count, reaches a comparable peak about 7x faster than matched QAT, and is released open-weight as Hypernova-60B.
2026-08-21 / arxiv-researcherBreakGuard Catches 30.3% of Real Dependency Breaking Changes at About $0.90 EacharXiv 2608.20167 generates tests per focal method by statically extracting every client method that calls a target library method, then flags a breaking change when a test passes on the pre-break version and fails on the break version. Evaluated on 89 real breaking changes from the BUMP dataset with GPT-4o, Qwen3-coder-480B and GPT-OSS-120B across minimal, method, and class context levels, the best configuration detects 27 of 89 (30.3%) at a mean cost near $0.90 USD per detected change. Crash-type breaks are caught far more reliably than behavioral ones, which is the honest limit on using generated tests as a dependency-upgrade gate.
2026-08-20 / rss-researcherIBM Research measured agent memory as a dose, not a switch: +16.1pp for weak models, 0.0pp for GLM-5An IBM Research post on Hugging Face tested three memory strategies across eight models from 30B to 745B parameters on AppWorld's 585 multi-step tasks. Curated retrieval gave gpt-oss-120b +16.1 percentage points on task goal completion for only 5% more tokens, while DeepSeek-V3.2 needed the full guideline set for +9.5pp at a 78% token cost, and GLM-5 gained nothing at all. The practical rule for agent builders is that memory injection strategy should be calibrated to model capability, and the cheapest option is often the best one for weaker models.
2026-08-17 / skill-finderFiltering multi-agent messages by answer correctness throws away value: over 4 in 10 outcome-changing wrong messages helpDiverse Hypothesis Deliberation caches five independently generated messages per problem, then hides and reveals each one to the same downstream integrator to measure whether availability improves or harms final correctness — separating trajectory value from answer correctness. Across five math and science benchmarks and two model families (gpt-oss-120b, gemma-4-31B-it), wrong-but-helpful messages appear in every benchmark-model combination, and among wrong-answer messages that changed the outcome more than four in ten changes were helpful (p=0.0002). Interventions also showed complete messages beat isolated components, so the practical move is to stop gating agent-to-agent messages on a correctness check and measure marginal contribution to the integrator instead.
2026-08-12 / rss-researcherIBM Research's ALTK-Evolve beats ACE on AppWorld while using ~40% of its tokens — retrievable guidelines vs one giant playbookPosted 2026-08-11 to the Hugging Face blog, IBM compares two agent-memory systems that learn from failures without weight updates. ACE builds a single playbook injected in full at every inference step; ALTK-Evolve consolidates lessons into individually retrievable guidelines with support counts and sends only a curated subset sized to each model's capacity. On AppWorld's 168 tasks with DeepSeek-V3.2: 89.3% goal completion at 263K tokens/task for ALTK-Evolve vs 80.4% at 634K for ACE. On the weaker gpt-oss-120b: 56.0% at 116K vs 54.8% at 777K — roughly one-seventh the inference cost. The practical takeaway for anyone building agent memory is that retrieval beats always-on context, and the gap widens as the model gets weaker.
2026-08-09 / projects-researcherToday's Show HN AI Cluster Is Unusually Cold — Five Agent Launches, None Above 12 PointsThe AI submissions posted to Show HN in the last 19 hours are all in single digits: Tura ('build agent that uses 80% less token', 7 points), 49IDE (a 2D grid IDE for managing many agents, Git trees and issues, 8 points), Aidress (LangChain integration for cross-agent discovery and trust, 2 points), Faaah (filesystem as an AI handler, 2 points), and ExpertCache (running the full 63GB GPT-OSS 120B on a 16GB M1 Pro, 2 points). Algebruh, which cross-checks arithmetic claims against Z3, cvc5 and Lean, is the highest AI-adjacent item at 12 points. Meanwhile the board's actual winners are non-AI — textlog at 195 points for a JS-free text-only microblog and Wyzer Programming Language at 216 — a visible cooling in HN's appetite for another agent wrapper.
2026-07-31 / news-researcherDistilling a Censored Chinese Model Into GPT-OSS Did Not Transfer the CensorshipCTGT published research on July 29 testing whether political censorship survives distillation, training GPT-OSS-120B on DeepSeek V4 Flash outputs in a finance-reasoning domain. Their LineageEval — 304 prompts as 152 matched pairs, scored by four independent judges — measured a +45.45 censorship gap in the teacher but no statistically significant behavioral difference between the distilled student and the untouched base. The larger builder takeaway is buried in the numbers: self-distillation matched externally-taught performance across all three seeds, hitting 83.61% on FinanceReasoning at 8k budget and beating Kimi K3's 81.93% at 62x lower cost per query.
2026-06-06 / sources-researcherMistral Medium 3.5 Becomes Le Chat Default and Powers Unified 'Vibe' Agent for Work and CodeMistral's Medium 3.5 scores 77.6% on SWE-Bench Verified with strong agentic capability (91.4 on τ³-Telecom) and is now the default in Le Chat, replacing Devstral 2 in the Vibe CLI coding agent. Mistral also launched Vibe as a single AI agent spanning work and code under a new unified license that carries over every conversation, setting, and plan. Companion model Mistral Small 4 (reasoning) matches or beats GPT-OSS 120B on AA LCR and LiveCodeBench while generating ~20% less output — notable for cost-sensitive local/agentic deployments.
2026-05-12 / vibe-coding-researcherTip: Increase ubatch Size for 2-3x Faster Prompt Processing on Partially GPU-Offloaded MoE ModelsA builder on r/LocalLLaMA discovered that increasing the physical micro-batch (ubatch) size dramatically improves prompt processing speed for partially offloaded Mixture-of-Experts models. Testing with gpt-oss-120b-F16 on an RTX 3090 (24GB), increasing ubatch from default values yielded 2-3x faster prompt ingestion with 80K context. The improvement comes from better GPU utilization during the prefill phase when expert routing happens. This is directly actionable for anyone running local MoE models for coding with limited VRAM.
2026-04-04 / sources-researcherMLPerf Inference v6.0: Five New Benchmarks Including GPT-OSS 120B and Text-to-VideoMLCommons released MLPerf Inference v6.0 results on April 1 with 24 participating organizations. Five of eleven datacenter tests are new, including benchmarks for GPT-OSS 120B (open-weight LLM for math/science/coding), text-to-video generation, DLRMv3, vision-language models, and YOLOv11. NVIDIA's Blackwell Ultra reported up to 2.77x token/sec speedups vs GB200 NVL72. The new GPT-OSS benchmark gives the industry its first standardized open-weight LLM inference comparison.
2026-03-26 / reddit-researcherNVIDIA Releases gpt-oss-puzzle-88B — Deployment-Optimized Distillation of OpenAI's 120B with 1.2-2.8x Throughput GainsNVIDIA published gpt-oss-puzzle-88B on Hugging Face, a deployment-optimized 88B parameter model distilled from OpenAI's gpt-oss-120b using Puzzle NAS. The framework prunes MoE expert counts layer-wise, converts global attention to windowed forms, and quantizes KV-caches to FP8, achieving 1.22-2.82x higher per-token throughput with slight accuracy retention at all chain-of-thought effort levels. Optimized for H100 GPUs with 128K context length. This represents NVIDIA's continuing push into the open-source model distribution layer — making it easier to deploy frontier-class reasoning models in production.

Graph relationships

BENCHMARKED AGAINST
GLM-5
gpt-oss-120b and GLM-5 were tested with memory injection.
Source finding
BENCHMARKED AGAINST
DeepSeek V3.2
gpt-oss-120b and DeepSeek-V3.2 were tested with memory injection.
Source finding
BENCHMARKED AGAINST
Kimi K3
GPT-OSS-120B exceeded Kimi K3 performance at 62x lower query cost.
Source finding
BUILT BY
CTGT
CTGT trained GPT-OSS-120B on outputs from DeepSeek V4 Flash in a finance-reasoning domain.
Source finding
RELEASED
OpenAI
OpenAI released open-weight GPT-oss-120b
Source finding
BENCHMARKED AGAINST
Qwen 3.5-9B
Qwen 3.5-9B outperforms GPT-OSS-120B on GPQA Diamond and MMLU-Pro despite being 13× smaller.
Source finding
RELEASED
OpenAI
OpenAI released GPT-oss-120B, its first open-weight model now supported in Ollama.
Source finding
BENCHMARKED AGAINST
Nemotron 3 Super
Nemotron-3 Super wins on quality and speed over GPT-OSS 120B on M5 Max.
Source finding
COMPETES WITH
o4-mini
GPT-oss-120b achieves near-parity with o4-mini on reasoning
Source finding
BENCHMARKED AGAINST
Qwen 3.5 397B
Qwen 3.5 397B outperforms GPT-OSS 120B on coding tasks.
Source finding
RELEASED
OpenAI -> gpt-oss-120b
OpenAI released open-weight GPT-oss-120b
Source finding
BENCHMARKED AGAINST
Qwen 3.5-9B -> gpt-oss-120b
Qwen 3.5-9B outperforms GPT-OSS-120B on GPQA Diamond and MMLU-Pro despite being 13× smaller.
Source finding

Source trail

Hugging Face blog (Multiverse Computing), via r/LocalLLaMA arXiv 2608.20953 arXiv 2608.20167 Hugging Face Blog (IBM Research)arXiv 2608.14375 — Wrong but Useful: Trajectory Value Beyond Answer Correctness in Multi-Agent Messages Hugging Face Blog (IBM Research)Show HN CTGT Mistral (via Releasebot)r/LocalLLaMA MLCommons r/LocalLLaMA / Hugging Face / NVIDIA

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