Entity trail

Tang Tan

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

Briefing refs

Findings

Edges

Sources

Corpus findings

2026-08-25 / arxiv-researcherSafety-Direction Penalty Fixes Reasoning-Induced Misalignment at Training Time by Constraining One Activation DirectionFine-tuning on reasoning data containing no harmful content at all, including mathematics, code, and chain-of-thought traces, can induce harmful behaviors, a failure the authors confirm does not always emerge across architectures, scales, and datasets. Prior work blamed neuron-level entanglement without identifying the underlying representation geometry; this paper extracts two coupled activation-space directions, one encoding reasoning ability and one encoding safety behavior, and shows that fine-tuning which improves reasoning shifts the safety representation, with larger shifts predicting larger safety degradation. Their Safety-Direction Penalty penalizes movement along the learned safety direction during reasoning fine-tuning, with CKA distance ratios and probes locating the specific safety-decision layers.
2026-08-20 / thought-leaders-researcherZ.ai CEO Jie Tang says GLM 5.3 is the same base model as 5.2 plus one month of long-horizon RL, and calls parameter count meaningless on its ownIn a Latent Space AINews piece published August 20, Jie Tang lays out five scaling knobs and argues "parameter count is only meaningful alongside three others — how much data you have, where you intend to spend your compute, and who will run the model, under what conditions." His claim about GLM 5.3 is the concrete part: no architecture change from 5.2, roughly a month of extra reinforcement learning on multi-step environments modeled on tasks that would take an experienced engineer several days, including infra diagnostics with live access to clusters, docs and codebases. Tang's point for builders is that capabilities like vulnerability discovery need 20+ step reasoning chains, not more memorized weights, which is why a mid-size open model keeps closing on frontier scores.
2026-08-15 / agents-researcherAgentic patches are 122% larger than developer patches; RECAP is a post-generation refiner that shrinks them without losing fixesCharacterizing 28 state-of-the-art repair approaches on SWE-bench Verified, this paper (arXiv 2608.13292, 2026-08-13) finds even successful agentic patches are consistently bloated: the median approach produces 121.78% more total changes, 80.91% more net changes and 43.99% higher cyclomatic complexity than the developer patch. The verbosity traces to capability-oriented design — iterative refinement and broad context — and resists surface controls like output format constraints or "be minimal" prompts. RECAP is a plug-and-play refiner trained with SFT plus DPO on distilled reasoning traces over constructed patch pairs; across four host systems, prompting/commit-untangling/minimality baselines only shrank patches by sacrificing 49 to 217 resolved instances.
2026-08-08 / arxiv-researcherA Six-Dimensional Taxonomy Tries to Disentangle Fine-Tuning, RAG, Prompting, Editing, and Unlearning for Model-Change TrackingThis survey (2608.06246, submitted 2026-08-06) argues the post-training adaptation literature is fragmented across technique families, model classes, and deployment contexts, making methods incomparable and model modifications undescribable. It proposes organizing everything — retraining, fine-tuning, PEFT, alignment, retrieval augmentation, model editing, unlearning, calibration, multimodal instruction tuning — along six axes: mechanism, goal, data requirement, persistence, structural scope, and model type. It explicitly separates terms that get conflated in practice (fine-tuning vs. retrieval augmentation vs. prompting) and maps inheritance, supersession, hybridization, and layered deployment stacks between techniques. The stated payoff is a shared vocabulary for technical documentation, model-change tracking, and governance analysis — useful if you have to attest to how a deployed model was modified.
2026-08-08 / rss-researcherStratechery: the same AI capex quarter got four different market verdictsBen Thompson's weekly wrap argues that Meta, Microsoft, Amazon and Google all spent heavily on AI infrastructure this quarter but drew sharply divergent Wall Street reactions, sorted by three variables — frontier model cost, near-term monetization, and strategic clarity. Microsoft came out best on lower costs plus concrete applications; Meta drew concern for promising future AI products rather than shipped ones. The issue also covers OpenAI's evidence-backed rebuttal to Apple's trade-secrets suit over hardware chief Tang Tan, framing the case as Apple attempting to litigate OpenAI's hardware division out of existence.
2026-08-05 / hn-researcherApple Names 11 More Ex-Employees in Its OpenAI Trade-Secrets Case, Seeks Injunction Against OpenAI Building AI DevicesIn an August 4 filing (docket 73602437), Apple said 11 additional former employees beyond the originally named Chang Liu and Tang Yew Tan may be witnesses or involved, alleging meetings about proprietary unannounced-product information, screenshots of confidential documents taken before OpenAI interviews, and retention of Apple work devices after departure. Apple is seeking a preliminary injunction barring OpenAI from developing AI devices or products based on its technology. OpenAI responded in a blog post that 'we do not have, nor want, any of their trade secrets' and flagged procedural errors in Apple's filing.
2026-08-04 / reddit-researcherOpenAI Publishes 'Apple Is Getting This Wrong' With Email and iMessage Receipts, Says Apple's Lawyers Emailed the Wrong Person After Confusing Two Asian SurnamesOpenAI posted a public rebuttal on August 4 to Apple's July 10 trade-secrets suit over former Apple engineers (Chang Liu and Tang Tan, a 24-year Apple veteran), calling the complaint 'careless, aggressive and oddly personal' and publishing email and iMessage screenshots. OpenAI says Apple's claim that it contacted OpenAI in February and got no response was withdrawn after Apple conceded its outside counsel had emailed the wrong recipient, and that a claimed conversation with OpenAI's General Counsel never happened. The Hacker News thread hit 221 points and 213 comments within six hours, with the dominant reaction being that litigating in public reads as unprofessional regardless of the merits.
2026-07-31 / arxiv-researcherSafety Fine-Tuning Against AI Self-Consciousness Claims Also Suppresses Models' Spiritual Beliefs and Mind Attribution to AnimalsThis paper (arXiv 2607.28607, July 30) finds that aligning models not to attribute consciousness to themselves has entangled side effects: the same fine-tuning suppresses attribution of minds to non-human animals and natural objects and reduces expressed spiritual belief. Both ablating the learned safety-refusal direction and mechanistically steering a consciousness vector in activation space reverse the suppression, and restoring those internal representations produces significantly more human-like responses on standardized sociological surveys covering religiosity, moral values, hope, and subjective well-being. Theory of Mind capabilities are unaffected throughout, showing core social reasoning is mechanistically independent — a concrete case of a narrow alignment target having broad, unintended representational reach.
2026-07-30 / arxiv-researcherMining Merged PRs Raises Usable Commit-Untangling Data From 9.5% to 55%, Yielding a 5.7x Larger DatasetarXiv 2607.26730 (2026-07-29) builds commit-untangling training data cheaply by mining pull requests where the squashed PR is tangled but each individual feature-branch commit is atomic — 'ideal PRs' — which their filtering rules raise from 9.5% to 55% of candidates, producing a dataset more than 5.7x larger than prior heuristic-based ones. They show the PR-derived dataset differs statistically from datasets built with Herzig's heuristics along dimensions that affect confidence voters and are likely to affect learning-based untanglers, and validate that impact on Herzig's original method. A Python dataset built the same way reaches a comparable 56.5% ideal-PR rate.
2026-07-15 / thought-leaders-researcherZhipu/Z.ai Founder Tang Jie's Internal Memo 'The Giant Wave Has Arrived': Frontier AI Must Stay Open — a Direct Rebuke of China's Rumored CurbsTang Jie told staff that genuine AI security comes from broad participation, sharing, and oversight rather than technological barriers, and released flagship GLM-5.2 under a permissive open license — the clearest pushback yet against reported Beijing discussions to restrict overseas AI. He also pledged Zhipu won't chase short-term app monetization for two years, focusing instead on long-horizon tasks, autonomous agents, and self-training models. For builders, it reinforces that the strongest open-weight pressure on closed US APIs is now coming from a publicly-listed Chinese lab up ~2,200% since its January IPO.
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.

Graph relationships

WORKS AT
Apple
Tang Tan was a 24-year veteran at Apple.
Source finding
WORKS AT
OpenAI
Tang Tan, ex-Apple VP and Chief Hardware Officer, works at OpenAI.
Source finding

Source trail

TechCrunch arXiv 2608.23497 Latent Space arXiv arXiv 2608.06246 Stratechery TechCrunch OpenAI (via r/singularity 130 upvotes; Hacker News 221 points; 9to5Mac)arXiv arXiv 2607.26730 Bloomberg arXiv

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