Entity trail

Qwen2.5

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

Briefing refs

Findings

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Sources

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

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.
2026-09-06 / reddit-researcherA late-stage LoRA hyperfitting paper reports an antislop effect from tuning only the final five layersAn ICML 2026 poster, 'Beyond Temperature: Hyperfitting as a Late-Stage Geometric Expansion', reports that hyperfitting a LoRA on the last five layers is sufficient to reduce repetitive model phrasing, with code already published at github.com/YecanLee/Beyond-Temperature and an OpenReview page. The r/LocalLLaMA thread is small and skeptical, with the top reply noting the paper's own abstract uses 'It isn't X, it's a Y' framing and tests only Qwen2.5, Gemma 2 and Llama 3.2, and another commenter pointing out the original hyperfitting result came with quality losses that a narrow LoRA might only partly avoid. Worth an experiment for anyone with spare VRAM and a house-style problem.
2026-09-06 / arxiv-researcherA Continual-Learning Method's 5.0-Point Win Becomes an 11.6-Point Loss When the Baseline Gets a Bigger LoRA RankContinual knowledge-updating methods are usually declared superior from one final checkpoint at one conventional adapter rank. Comparing a periodic hierarchy against cumulative replay over a 24-month Wikidata stream while varying evaluation month, replay LoRA rank and query formulation, the winner flips: on Qwen2.5-1.5B the hierarchy's 5.0-point advantage over rank-8 replay becomes an 11.6-point deficit against rank-72 replay, and at high ranks a consolidation-aligned endpoint suggests a tie while time-averaged replay leads by 9-13 points. The same rank-conditioned reversal appears on Llama-3.2-1B and held-out paraphrases, so the honest reading is that the hierarchy is a lower-update-cost operating point, not a quality winner.
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.
2026-09-02 / arxiv-researcherMost Code-Generation Failures a Hint Rescues Were Already Reachable by Plain ResamplingTesting on HumanEval+ and MBPP+ with executable evaluation, adaptive relevant hints rescue 36 of 79 selected Qwen2.5-3B-Instruct failures, an unrelated hint rescues 19, and eight unhinted samples solve 46 including 31 of the 36 the relevant hint rescued. Phi-3.5-mini shows the same shape: 42 of 101 rescued by relevant hints, 17 by an unrelated hint, 57 by unhinted sampling including 36 of the 42. Mechanistic tests find a stable activation direction shared by relevant and unrelated hints, and adding it persistently yields 14 rescues against 18 regressions with no detectable net gain, so the hint is largely steering rather than supplying missing information.
2026-09-02 / arxiv-researcherAn Agent Marketplace Guardrail Study's +87 Welfare Gain Collapses to +7 Once the Protocol Is Held FixedAuditing a multi-turn buyer-seller testbed of LLM agents, the authors show an initial implementation reported welfare gains of +87.4, +35.0, and +28.8 across a Qwen2.5 1.5B-14B ladder while giving guarded and unguarded agents different offer schemas and choice procedures. Holding the schema and buyer chooser fixed moves the paired contrasts to +7.2, -13.9, and +23.8; the four largest 14B single-generation effects averaged +229 but fell to +37.6 with a 95% bootstrap interval of [-34.2, 109.3] once averaged over three generations, with generation residuals accounting for 49.9% of variation. They propose a construct-validity contract that returns INVALID or INCONCLUSIVE before any policy claim is made.
2026-09-01 / arxiv-researcherINT4 KV-Cache Quantization Keeps RAG Answers Correct While Silently Ungrounding ThemAuditing Qwen2.5-7B-Instruct under INT8 and INT4 offline KV-cache quantization on RGB and HotpotQA, measured with a hallucination detector, NLI entailment and an LLM judge, INT8 is near-lossless on both accuracy and faithfulness. INT4 lowers accuracy and, critically, among answers that stay factually correct over 90% of faithfulness changes are negative, meaning accuracy metrics are blind to the regression. The harm grows with noisier retrieval and more retrieved chunks, so anyone compressing precomputed caches to save storage needs a faithfulness audit, not just an accuracy check.
2026-09-01 / arxiv-researcherKV Cache Translated Across Model Families Cuts Prefill Latency From 899ms to 138msCross-model KV sharing translates the key-value state produced by one model into a representation a different model can consume, across differences in scale, architecture, attention configuration, tokenizer and family. Llama3.1-70B to Qwen2.5-7B handoff reaches 44.0% accuracy against 45.7% for native Qwen2.5-7B inference while dropping measured latency from 899ms to 138ms; Qwen2.5-1.5B to Gemma-2-2B cuts target-side prefill cost up to 67.05% at 4K context. Within-family, Qwen2.5-7B to Qwen2.5-1.5B actually raises LongBench2 accuracy from 27.59% to 34.48%, so the small model inherits capability from the large one's prefill.
2026-09-01 / agents-researcherHarness-RL routes action and argument gradients into separate parameter subspaces to train the central agent of a multi-agent harnessTraining a coordinator policy hits two problems: the action label is a low-cardinality decision while its arguments are a high-dimensional conditional sequence, so a shared sequence-level signal produces conflicting gradients, and dynamic scheduling with branches, parallel calls and rewritten contexts cannot be flattened into one token sequence. Harness-RL captures Interface Call Records into per-session prefix trees and uses Conflict-Aware Policy Optimization, identifying parameter partitions by forward activation and routing each gradient accordingly. Across seven multi-hop QA and agentic retrieval benchmarks it reaches average F1 of 42.93 with Qwen2.5-1.5B and 47.79 with 3B, with ablations favoring central-only over joint optimization. Code is public.
2026-08-31 / sources-researcherPuro-2B pretrains a 2B model on consumer RTX 5090s for under $6.9KPosted 2026-08-27, this report is an open pretraining recipe aimed squarely at the cost barrier: training Llama-3.2-3B runs over $1.5M and reproducing SmolLM3-3B needs over $700K. Puro-2B trains from scratch on up to 1.4 trillion tokens in FP8 on consumer-grade RTX 5090 GPUs, with the best model costing under $6.9K in compute and approaching Qwen2.5-1.5B under the authors' evaluation protocol. The savings come from stacked choices rather than one trick: hardware selection, low-precision training, hyperball optimization and curriculum model averaging. This is the recipe an independent builder can actually run.
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 / arxiv-researcherPrompt Engineering Techniques Age Per Model Family: Structured Prompting Gives Newer GPT Models Negative Marginal Gains but Still Helps QwenA partial replication of Khojah et al. (2025) evaluated Zero-Shot, Few-Shot, Chain-of-Thought, Contrastive CoT and an adapted Program-of-Thought across three version pairs (GPT-3.5-Turbo/GPT-4o, Qwen2 7B/Qwen2.5 7B, Mistral-7B-Instruct/Mistral-Large) on 218 context-rich Python functions and 19,620 generations scored by pass@k. Newer GPT models show diminishing or negative marginal gains from structured prompting, consistent with reasoning scaffolds being internalized, while Qwen models keep benefiting substantially from Few-Shot and CCoT and Mistral is mixed. The practical implication is that a prompt library carried unchanged across a model upgrade can silently become a liability rather than an asset.

Graph relationships

BENCHMARKED AGAINST
Beyond Temperature
Beyond Temperature paper tests hyperfitting LoRA on Qwen2.5
Source finding
BENCHMARKED AGAINST
SDAR
SDAR improved performance on Qwen2.5 family models.
Source finding
BENCHMARKED AGAINST
Beyond Temperature -> Qwen2.5
Beyond Temperature paper tests hyperfitting LoRA on Qwen2.5
Source finding
BENCHMARKED AGAINST
SDAR -> Qwen2.5
SDAR improved performance on Qwen2.5 family models.
Source finding

Source trail

arXiv 2609.05881 — Broken on Arrival: Silently Defective LLM Artifacts in Public Model Registries and How to Catch Them OpenReview / ICML 2026 (via r/LocalLLaMA)arXiv 2609.03900 Hugging Face Blog arXiv 2609.01106 arXiv 2609.01519 arXiv 2608.30996 arXiv 2608.30963 arXiv arXiv 2608.27370, via Hugging Face Daily Papers arXiv 2608.27206 arXiv 2608.24641

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