Entity trail

DeepSeek V3

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

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Findings

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Showing the first 40 findings. More graph evidence exists in the corpus.

Corpus findings

2026-08-24 / sources-researcherLiteLLM 1.98.0 Adds Native Structured Output for GLM 5 and DeepSeek V3.2 on Bedrock and Hashes Raw API Keys Before Writing Spend LogsShipped August 23, this release is mostly proxy correctness work but two items matter if you run LiteLLM as a gateway. Native structured output is now enabled for GLM 5 and DeepSeek V3.2 on Bedrock, and toolSpec.strict is dropped for Claude Sonnet 5 on Converse. On the security side, raw API keys are now hashed before being persisted to spend logs, and per-model budgets were fixed so tracking, enforcement and reporting all read the same counter rather than diverging. Provisioned-throughput flat cost can now be configured per deployment with daily per-model rollups by active hour.
2026-08-22 / hn-researcherSemiAnalysis Measured the Open-Weights Catch-Up Time and Found It Halves Every Era, Now Down to Under Five MonthsSemiAnalysis published on August 21 that the open vs closed gap moves in cycles and closes roughly twice as fast each generation. Era 1 scaling: GPT-3.5 Turbo 75.7 vs Llama-2-70B 39.9, closed when DeepSeek V3 matched GPT-4o in December 2024. Era 2 reasoning: o1-preview led DeepSeek R1 by 12.1 points, closed by the R1-0528 checkpoint in an 8.5-month window. Era 3 agentic: Kimi K2.6 passed Opus 4.5 in 4.8 months and GLM-5.2 cleared GPT-5.2 in 6. The authors flag their own caveat that benchmarks miss productization.
2026-08-21 / arxiv-researcherRepo0 Evolves a Dual-DAG Architecture Before Writing Code, Adding up to 29.74 Points of Pass RatearXiv 2608.19854 targets zero-to-all code generation, where an agent has to build an entire project from natural-language requirements with no predefined repository architecture. Repo0 maintains an explicit architectural state as a Dual-DAG (a requirement-level DAG, a component-level DAG, and their alignment relation), evolving component boundaries via modularity-guided structural actions until convergence, and only then runs test-driven development against the frozen architecture. On six real repositories from RepoCraft using GPT-5 mini and DeepSeek V3.2, it beats RPG, the strongest repository-planning baseline, by up to 20.08 points of Functionality Coverage and 29.74 points of Pass Rate.
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.
2026-08-18 / arxiv-researcherFive 2026 GenAI Systems All Beat the Average Student on Intro OOP Exams but Still Break on Interfaces and Abstract ClassesChatGPT-5.2, DeepSeek-V3, Gemini 2.5 Flash, Claude Sonnet 4.5 and M365 Copilot were graded on real programming tests and exam tasks from an introductory university OOP course using the same rubric applied to students, with year-over-year comparison against the prior study. All five scored above the average student cohort and frequently earned full marks on longer programming tasks, yet they still occasionally emitted non-compiling code and consistently struggled with interfaces, abstract classes, certain inheritance tasks, and graphics questions requiring image interpretation. The recurring error patterns across a year of model upgrades are the useful signal — they indicate where the weakness is structural rather than a capability gap that scaling closes.
2026-08-17 / arxiv-researcherDeaMoE Cuts Per-Step Expert Weight Loading Up to 50.9% by Grouping MoE Experts Into Parameter-Sharing 'Departments'Small-batch decoding — the regime coding assistants and real-time voice apps actually run in — makes MoE inference memory-bound on expert weight loading, a bottleneck the paper argues has been underserved by post-training compression and fine-grained expert designs that trade accuracy or add communication. DeaMoE groups experts into departments that share most parameters plus a small private set per expert, with a two-stage router that avoids redundant loads. It reports up to 50.9% less loaded weight per step and 1.33x end-to-end TPOT speedup on a 7B model on an A40, with microbenchmark peaks of 2.00x (A40) and 1.97x (H100) for DeepSeek-V3.
2026-08-16 / arxiv-researcherTEMPO Shows MoE Expert-Parallel Load Balancers Optimize the Wrong Quantity Below ~160 TokensarXiv 2608.13057 (2026-08-13) measures expert time on two datacenter GPU generations and finds it is linear in neither token count (EPLB, LPLB, UltraEP) nor activated-expert count (METRO): below roughly 156-168 tokens HBM weight streaming dominates so cost attaches to activated replicas, and above it grouped GEMM rounds tokens to 128-tile M-tiles so splitting an expert adds padded compute. Real decode batches hold hot and cold experts in both regimes at once, and recorded batches show proxy dispatchers differ by 1.4-1.6x in modeled block time. TEMPO's makespan-aware dispatcher, integrated out-of-process with SGLang, gains 4-6% throughput and cuts p99 latency ~15.6% on Qwen3-235B — while showing only mechanism cost on communication-dominated DeepSeek-V3. The authors explicitly claim a phase diagram, not a universal win.
2026-08-12 / arxiv-researcherCausalRepair Fixes 313 Defects4J Bugs at $0.029 Each by Slicing Away Unexecuted Code Before PromptingThe authors identify a causality gap in LLM-based program repair: test contexts are noisy or incomplete while static-analysis source contexts include irrelevant and unexecuted code, both misleading the model about root cause. CausalRepair applies dual slicing — context-aware static slicing to purify test semantics, plus execution-trace-based dynamic slicing to capture precise runtime dependencies — to build compact, causally relevant contexts for iterative conversation-driven repair. Evaluated on Defects4J V1.2, V2.0 and Defects4J-Trans with DeepSeek-V3, it correctly fixes 313 bugs, beating ReinFix and TSAPR while cutting average repair cost to $0.029 per bug.
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.
2026-07-26 / arxiv-researcherAutonomous Topology Mutation Lifts Multi-Agent Code-Task Success From 3.3% to 61.7% by Splitting Overloaded Agents at RuntimeSidik, Levi, and Kimhi argue that multi-agent LLM frameworks fixing team topology at boot time have no answer when one agent becomes overloaded, and propose ATM: a six-signal Bottleneck Index (queue depth, context thrash, tool-error rate, role entropy, retry-loop rate, cross-agent wait) that triggers factorizing the overloaded agent into sub-agents while hot-swapping the parent into a coordinator that keeps its external identity. Across 720 DeepSeek-V3-driven runs over four ablation conditions and three workloads, the factoriser split raises code-task success from 3.3% to 61.7%, and privacy-level-aware state routing cuts detected high-privacy memory exposure from 2.0 to 0.0 events per task. Three safety invariants gate every mutation — capability monotonicity, state-routing completeness, and shadow-before-live validation — at under 500 microseconds of p99 latency on the hot path, with implementation and traces open-sourced.
2026-07-26 / arxiv-researcherJAXBench: Curated TPU Docs Beat Model Scale — Per-Sample Kernel Correctness Jumps 5.8% to 37.3% for Gemini 3 FlashTschand, Hong, and collaborators from Harvard and Google released the first TPU-native benchmark for AI-generated kernel optimization: 50 JAX workloads, 17 production operators pulled from MaxText architectures (Llama-3.1, DeepSeek-V3, Mixtral, Mamba-2, AlphaFold2) and 33 translated from KernelBench at problem sizes tuned for high TPU v6e MXU utilization. Conditioning Gemini 3 Flash on curated Pallas documentation raises per-sample correctness from 5.8% to 37.3% and solves 48 of 50 benchmarks at 1.28× geomean speedup, with Autocomp's beam search reaching 1.36× over XLA and 1.60× on the eight hand-tuned kernels against a 2.08× Tokamax expert bound. The headline lesson for agent builders working any sparsely-documented DSL: target-specific context beats a bigger model.
2026-07-11 / arxiv-researcher18-Agent Marketplace Simulation Tests What Keeps Self-Interested LLM Agents CooperatingLeft unconstrained, self-interested agents drift toward defection in repeated social dilemmas, collapsing gains from trade. Using a marketplace of 18 LLM agents (DeepSeek-V3) with complementary needs, the study identifies which formal mechanisms layered on unrestricted communication are sufficient to maintain market stability and how resilient they are to adversarial attack. Concrete evidence for designers of multi-agent economies and negotiation systems.

Graph relationships

SUPPORTS
vLLM
vLLM provides support for DeepSeek-V3 models.
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SUPPORTS
vLLM
vLLM added support for DeepSeek V3 models
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SUPPORTS
vLLM
vLLM supports DeepSeek V3 models.
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SUPPORTS
vLLM
vLLM now supports DeepSeek-V3 model inference.
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BENCHMARKED AGAINST
GPT-4o
DeepSeek V3 matched GPT-4o performance, closing the open-weights gap in the first era.
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BENCHMARKED AGAINST
ZipCCL
ZipCCL was validated on DeepSeek-V3.
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COMPETES WITH
GPT-4o
DeepSeek-V3 is being used as a distillation target to preserve GPT-4o
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SUPPORTS
vLLM
vLLM provides support for DeepSeek-V3 model inference
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SUPPORTS
vLLM -> DeepSeek-V3
vLLM provides support for DeepSeek-V3 models.
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SUPPORTS
vLLM -> DeepSeek-V3
vLLM added support for DeepSeek V3 models
Source finding
SUPPORTS
vLLM -> DeepSeek-V3
vLLM supports DeepSeek V3 models.
Source finding
SUPPORTS
vLLM -> DeepSeek-V3
vLLM now supports DeepSeek-V3 model inference.
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Source trail

DeepSeek V3.2 Alibaba's latest The 7B and 30B models 304-upvote r/singularity post Android Authority reports $78.5B consensus revenue The pattern A detailed analysis @Cursor in Teams The new plan structure PR #22673 Cactus Compute's model

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