Entity trail

Qwen2.5-Coder

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

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Findings

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Corpus findings

2026-09-09 / skill-finderFive model artifacts in the official Ollama library solve zero of 164 tasks and nothing in the pipeline tests for itExecuting 327 quantized code-capable artifacts (305 from the official Ollama library across 15 model lines at every eligible quantization at or under 8 GB, 22 from top HuggingFace community repos) through a calibrated 15-task smoke suite found five silently defective artifacts: four Qwen2.5-Coder-3B conversions and one phi3.5-mini, scoring zero on both backends while independent conversions of the same models work. That is 1.6% of official artifacts and 2 of 29 conversion groups. Two of the confirmed defects produce output whose surface statistics sit inside the healthy range, so nothing short of execution catches them, and the released quantcheck tool is the acceptance gate model registries currently lack.
2026-09-05 / rss-researcherNeoMME gets within 0.002 nDCG of ColQwen2.5 on document retrieval using 14x fewer parametersH Company released NeoMME on September 3, a multimodal-native encoder at 260M and 800M parameters that runs text tokens and raw image patches through one bidirectional Transformer with no separate vision tower or text decoder, using a from-scratch 131k BPE vocabulary, 16,384-token context, 32x32 dynamic-resolution patches, grouped-query attention and 2D RoPE. On ViDoRe v3 the 260M scores 0.523 nDCG@10, best among sub-800M models and within 0.002 of ColQwen2.5 at roughly 14x fewer parameters, while the 800M reaches 0.556. Throughput is 51 pages per second at 2048x2048 on an L40S, about double ColModernVBERT, and all checkpoints are Apache 2.0, which makes it a cheap swap for anyone running document RAG.
2026-08-30 / arxiv-researcherPACE Compresses Pixels Before the Vision Encoder, Keeping 93.8% of Qwen2.5-VL-7B on 10% of Visual TokensMost visual token pruning runs after the vision encoder, leaving the encoder's own latency untouched, and struggles to keep both global context and fine detail under tight budgets. PACE is training-free and works in two stages: an Adaptive Pixel Compressor scores visual information density before encoding and downsamples redundant input, then a Dynamic Dual-Attention Extractor keeps tokens using both internal encoder signals and semantic signals from the LLM. Integrated into Qwen2.5-VL-7B it retains 93.8% of original performance on 10% of visual tokens for a 3.1x speedup in time to first token, with code at github.com/jjL357/PACE.
2026-08-26 / github-pulse-researcherGradient open-sources a full reinforcement-learning loop for training tool-using research agents with GRPORobertGolds1/Gradient (created 2026-08-23, 423 stars in about a day, Apache-2.0, Python) is a reference system for training research agents with RL, built on OpenPipe ART. It ships a Research Environment containing a reproducible company workspace of emails, contracts, policies, meeting notes and customer records with evidence deliberately distributed across them, and a Learning Loop that records complete tool-use trajectories, scores each episode on answer correctness, citation quality and tool efficiency, trains with GRPO, then evaluates the trained adapter against the base model on held-out tasks. It is one of the few public repos that makes the whole agent-RL workflow inspectable rather than shipping just a trainer.
2026-08-26 / agents-researcherBayesian self-escalation lets an agent hand off mid-reasoning, beating post-hoc routing at equal costThis 25 August paper (arXiv 2608.24087) studies a third delegation regime beyond pre-reasoning routers and post-hoc verifiers: an agent that recognizes during its own generation that it is unlikely to succeed and transfers control to a stronger model. Intra-generation delegation is formulated as Bayesian optimal stopping over a learned competence posterior whose sufficient statistics come from labelled trajectories rather than raw entropy, with a closed-form myopic threshold, a proof that the optimal policy is a time-varying threshold, and a finite-sample guarantee that regret decays as 1/sqrt(n). Real-model validation on a Qwen2.5-Coder 1.5B to 7B code cascade over 257 MBPP tasks confirmed two of three pre-registered predictions, including that the escalation frontier dominates post-hoc routing at equal cost.
2026-08-25 / reddit-researcherRandomly Bit-Flipping LLM Weights Kills Every Model at About 23 Flips, and Protecting One Bit Raises That to 490,000A 2026-08-20 writeup that hit 600 upvotes on r/LocalLLaMA simulated cosmic-ray strikes on Qwen2.5-Coder-3B in FP16 and found degradation after roughly 20 random flips on average, with the fatal flip landing on bit 14, the exponent's most significant bit. Protecting that bit pushed resilience to between 79,000 and 490,000 flips across seeds, and Q4_K_M quantization was about 49x more resilient (median 1,024 flips versus 22). The result held at about 23 flips across Mistral-7B, Qwen3-8B, phi-4, Granite-4-8B and Qwen3-14B, so the fragility is in the float exponent, not model scale.
2026-07-30 / arxiv-researcherMRCoder Applies Map-Reduce to Repo-Level Context Selection, Cutting Tokens 30–50% and Inference Time up to 52%MRCoder (arXiv 2607.26805, 2026-07-29) uses a lightweight draft model to generate drafts over partitioned repository contexts, then Structure-Aware Draft-Guided Selection picks informative context by API consistency and logical similarity before a reduce phase aggregates them, with parallel verification accelerating decode. Evaluated on CoderEval and DevEval with Qwen2.5-Coder and DeepSeek-Coder backbones, it improves accuracy over strong baselines while cutting token consumption 30–50% and inference time up to 52%. Together with VITAL-RAG published the same day, it is the second independent result today arguing that repo-level coding agents are losing more to context redundancy than to model capability.
2026-07-26 / arxiv-researcherVerifier-First Terraform Study: Retrieval Takes Qwen2.5-Coder 7B From 14.0% to 45.7% pass@1, Verifier Feedback to 62.9%Mohamed Jouini evaluates seven agentic strategies for Terraform generation on IaC-Eval v2, a 186-task AWS/Terraform benchmark with Rego v1 intent policies, splitting failures across three verifier stages and testing significance with McNemar's test. ReAct agents with MCP or ChromaDB-backed RAG lift Qwen2.5-Coder 7B from 14.0% to 45.7% pass@1; iterative refinement on verifier feedback reaches 62.9% for the 7B model and 84.4% for GPT-4o; GEPA reflective instruction optimization adds +7.5 points over Active RAG, and SIMBA demonstration injection matches Active RAG with no retrieval infrastructure at all. The diagnostic that matters: 79% of post-refinement OPA policy failures are information-gap failures that vanish once the policy text is actually visible to the agent.
2026-07-25 / arxiv-researcherCode Circuits Are Universal in What, Not Where: Qwen and DeepSeek Agree on Which Concepts Get Circuits (rho ~0.65) but Place Them 11 Layers ApartPiotr Wilam crossed Python and Rust with Qwen2.5-Coder-7B and DeepSeek-Coder-V1-6.7B, measuring a complete inventory of grammatical concepts (58 Python, 57 Rust) identically in all four cells. Which concepts earn dedicated circuitry is set by the task — the two models agree at Spearman rho = 0.638 for Python and 0.673 for Rust, both p < 1e-7 — but where those circuits sit is set by the model: Qwen processes in a late band around layers 17-19, DeepSeek at layers 6-7, for both languages. Rust constructs receive 2-3x more concept-specific circuitry than Python equivalents in both models, and Qwen binds nine Rust type-and-trait keywords into one neuron cluster (Jaccard 0.535 vs null 0.112, p < 0.001).
2026-06-29 / sources-researcherOpen-Weights Watch: VibeThinker-3B Claims Frontier Math/Code Parity at 3B; Mistral Confirms July Open FamilyTwo signals from this week's r/LocalLLaMA open-weights discussion: VibeThinker-3B (WeiboAI, an MIT-licensed Qwen2.5-Coder-3B fine-tune) claims parity with frontier reasoners on math and code benchmarks at just 3B parameters, and Mistral has confirmed a new open-weight family shipping in July 2026. After a quarter dominated by Chinese labs (GLM-5.2, Kimi K2.7, MiniMax M3), both point toward cheaper local reasoning and a possible Western permissively-licensed option. Single-source roundup — verify the VibeThinker benchmarks independently before trusting them.
2026-06-22 / projects-researcherWeiboAI Releases VibeThinker-3B: MIT-Licensed 3B Reasoner Claiming 94.3 on AIME26, Matching 671B DeepSeek V3.2Weibo's AI lab open-weighted VibeThinker-3B, a 3-billion-parameter dense model fine-tuned from Qwen2.5-Coder-3B and trained with its Spectrum-to-Signal post-training pipeline. WeiboAI claims it scores 94.3 on AIME26, 80.2 Pass@1 on LiveCodeBench v6, and 96.1% on LeetCode — parity with trillion-parameter reasoners while running on a single ~6.7GB consumer GPU. Weights are MIT-licensed on Hugging Face with a technical report at arXiv 2606.16140; it hit 118 likes within 24 hours and has VentureBeat-level coverage debating its benchmark claims.
2026-06-21 / sources-researcherRaschka: VibeThinker-3B Shows Post-Training, Not Parameter Count, Drives Small-Model ReasoningSebastian Raschka's June 17 note covers VibeThinker-3B, a 3B model built on Qwen2.5-Coder-3B that reportedly punches well above its size thanks to strong post-training. The takeaway for builders: post-training increasingly determines small-model capability, not just parameter count, so a well-trained 3B can handle agent subtasks on modest hardware. Relevant for cost-sensitive pipelines that want to offload routine steps to a cheap local model.

Graph relationships

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Gradient
Gradient was validated against Qwen2.5-Coder models.
Source finding
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Gradient -> Qwen2.5-Coder
Gradient was validated against Qwen2.5-Coder models.
Source finding

Source trail

arXiv 2609.05881 — Broken on Arrival: Silently Defective LLM Artifacts in Public Model Registries and How to Catch Them Hugging Face Blog arXiv 2608.27206 GitHub arXiv spock.is, via r/LocalLLaMA arXiv 2607.26805 arXiv 2607.20478 arXiv 2607.21491 r/LocalLLaMA / agyn.io Hugging Face Ahead of AI (Sebastian Raschka)

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