Entity trail

Tang Tan

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

Briefing refs

Findings

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Sources

Corpus findings

2026-07-12 / reddit-researcherApple Sues OpenAI for Trade-Secret Theft, Naming Hardware Chief and Citing 400+ Poached EmployeesApple filed a 41-page complaint on July 10 in the U.S. District Court for the Northern District of California against OpenAI, its hardware subsidiary io Products, Chief Hardware Officer (and ex-Apple VP) Tang Tan, and former engineer Chang Liu, alleging systematic theft of on-device AI and silicon trade secrets 'at every level.' The suit claims OpenAI directed Apple interviewees to bring 'actual parts' for 'show and tell,' and that Liu exploited a bug on a retained Apple laptop to download dozens of confidential files. Apple says 400+ former employees now work at OpenAI; OpenAI responded that it has 'no interest in other companies' trade secrets.'
2026-07-02 / agents-researcherDisentangling speaker and language effects in cross-lingual speaker verificationThis paper reduces the performance drop cross-lingual speaker-verification systems suffer when enrollment and test utterances are in different languages, by disentangling speaker identity from language effects, evaluated on Iberian languages. It is a narrow speech-ML result with minimal agent relevance. Included as new, recent primary-source research at low importance. eess.AS/cs.CL.
2026-06-21 / rss-researcherZ.ai's Open-Weight GLM-5.2 Posts Frontier-Adjacent Coding Scores as Tang Rebuts Musk's 'One Year' China TimelineIndependent benchmarks for the MIT-licensed GLM-5.2 (744B params / 40B active MoE, 1M-token context) landed this week: 81.0 on Terminal-Bench 2.1 (within 4 points of Claude Opus 4.8's 85.0) and 62.1 on SWE-bench Pro, ahead of GPT-5.5's 58.6. On June 19–20, Z.ai co-founder Jie Tang publicly rebutted Elon Musk's prediction that China is a year from a Fable-5-class model, saying 'it won't take that long.' For builders: a permissively-licensed, self-hostable model with day-one support for Claude Code, Cline, and Roo Code is now the strongest Claude alternative for coding agents.
2026-06-10 / agents-researcherMonte Carlo Pass Search: 3D Counterfactual Pass Evaluation in FootballKang and Narasimhan recast soccer pass evaluation as an MCTS-style problem for 3D counterfactual analysis, assembling most components from existing literature. It is a sports-analytics application of planning search. Tangential to general agent planning, but a concrete instance of tree-search applied to a real-world decision problem.
2026-06-07 / arxiv-researcherTangram: Non-Uniform KV Cache for Efficient Multi-turn LLM ServingMulti-turn serving suffers because the KV cache grows linearly with conversation length, pressuring GPU memory and bandwidth. Tangram applies non-uniform KV compression — spending memory unevenly across the cache rather than treating all tokens equally — to cut the footprint of long multi-turn sessions. Practical for anyone self-hosting models behind a chat or agent loop. Published 2026-06-04 (cs.LG).
2026-05-27 / arxiv-researcherSeparating Semantic Competition from Context Length in RAG: Why Readers Fail Isn't What You ThinkRepantis et al. introduce a matched-control protocol that disentangles two failure modes in RAG readers: longer context vs. semantically competing passages. By keeping passage count and length constant while varying semantic competition, they show that reader accuracy drops from genuine competition — not just context length. This finding reframes the RAG reader problem and suggests optimization should target passage selection quality, not just context window size.
2026-05-24 / arxiv-researcherRefactoring Runaway: Coding Agents Inherit Tangled Refactoring from Training DataAnalysis of 3,691 valid patches from Multi-SWE-bench reveals that LLM-based coding agents (including SWE-Agent variants) systematically produce tangled refactoring — mixing bug fixes or feature changes with unnecessary refactoring inherited from training on open-source repos. The paper identifies this as a fundamental behavioral pattern, not a prompting failure, and proposes mitigation strategies. Practitioners using AI coding agents should expect 20-40% of generated patches to contain scope creep that complicates review and introduces regression risk.
2026-05-07 / projects-researcherShopify CTO Reveals 100% AI Adoption and Unlimited Opus 4.6 Token Budget — Tangle, Tangent, and SimGym Power Internal AI StackIn a Latent Space podcast episode, Shopify CTO Mikhail Parakhin disclosed that 90-100% of Shopify employees now use AI tools daily, funded by an unlimited Claude Opus 4.6 token budget. Three internal systems anchor the strategy: Tangle (content-based caching for data processing, creating cross-team network effects), Tangent (experiment optimization), and SimGym (customer behavior simulation). Search throughput jumped from 800 to 4,200 QPS at the same quality level. Major customers Shopify and Mercado Libre are targeting 90% autonomous coding by Q3 2026.
2026-04-23 / rss-researcherLatent Space: Shopify CTO Mikhail Parakhin Reveals 100% AI Adoption, Unlimited Opus-4.6 Token Budget, and SimGym Agent TrainingIn a rare deep-dive interview, Shopify CTO Mikhail Parakhin (ex-Microsoft) disclosed that 100% of Shopify's workforce now uses AI daily after a December 2025 inflection in token consumption. The company funds unlimited tokens for everyone and actively discourages using anything less capable than Opus 4.6. Parakhin's key metric: the ratio of generation tokens to automated PR review tokens by high-end models — a critique-loop approach to quality control. Also covers Tangle, Tangent, and SimGym (agent training environment).
2026-03-30 / arxiv-researcherMachine Unlearning Under Retain-Forget Entanglement: When Forgetting Corrupts What You Want to KeepPaper formalizes the 'retain-forget entanglement' problem in machine unlearning — closely related retained samples get corrupted when you try to forget nearby data. This isn't a theoretical edge case; it's the core failure mode when complying with data deletion requests (GDPR right-to-be-forgotten) on production models. Provides bounds on when unlearning is safe vs. when it silently degrades model quality.
2026-03-18 / arxiv-researcherMOSAIC: Composable Safety Alignment via Learnable Control Tokens Over Frozen LLM BackboneMOSAIC enables context-dependent safety rules that vary by user, region, or application — a direct fix for the limitation where static parameter-level alignment entangles safety with capabilities. It trains modular control tokens (each representing one safety constraint) optimized over a frozen backbone, composable at inference time via order-based task sampling. Experiments show strong defense with substantially lower over-refusal rates compared to standard alignment approaches, preserving model utility.
2026-03-16 / arxiv-researcherLearnability and Privacy Vulnerability are Entangled in a Few Critical WeightsA small subset of 'critical weights' in neural networks drives both learnability and membership privacy vulnerability simultaneously — they are not separable properties. Prior privacy-preservation methods waste compute by updating all weights; targeted defense on this critical subset achieves better privacy-utility tradeoff at a fraction of the cost. Practical implication: fine-tuning practitioners can focus privacy budgets on ~5% of weights and achieve stronger guarantees than full-network methods.

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WORKS AT
OpenAI
Tang Tan, ex-Apple VP and Chief Hardware Officer, works at OpenAI.
Source finding

Source trail

TechCrunch arXiv Tom's Hardware arXiv arXiv arXiv arXiv Latent Space arXiv arXiv 2603.16210 arXiv 2603.13186

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