Entity trail

Qwen2.5-7B-Instruct

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

Briefing refs

Findings

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Corpus findings

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.
2026-08-26 / 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.
2026-08-22 / reddit-researcherMeasured Across 9 Models: Telling an LLM to Be Concise Saves ~1.5x, Shortening Your Prompt Costs Up to 96% MoreResearchers posting to r/MachineLearning tested output compression against input compression at five reduction levels on five short-answer datasets, across GPT-4o, GPT-5.4, Claude Haiku 4.5, Claude Sonnet 4.6, Qwen2.5-VL-7B, Qwen3.5-9B, DeepSeek-R1-Distill, Gemma-4-E4B and Kimi-K2.6. Instructing shorter output averaged 1.5x cheaper and up to 3x in the best case with accuracy roughly flat, and the effect held across an eleven-language run including Swahili, Bengali, Thai and Telugu. Trimming the input prompt did the reverse, costing up to 96% more on the worst benchmark because the model compensates with longer answers. The authors surfaced the paper specifically because Claude Code had shipped a concise output style the day before.
2026-08-08 / agents-researcherMERIT: training-free repair memory helps Text-to-SQL agents modestly — and the paper says plainly where it doesn'tMERIT (arXiv 2608.05906, submitted 2026-08-06) keeps an online dual-polarity memory of oracle-verified corrections and observed dead ends, classifies each failure into a coarse type, and conditions a hybrid lexical-dense retriever before a frozen Qwen2.5-7B-Instruct generates each revision. Execution accuracy rises from 66.34% to 69.79% on Spider and 47.35% to 48.44% on BIRD versus stateless iterative repair. The honesty is the value: paired analysis supports the Spider gain but is weak on BIRD, MERIT is not reliably separated from untyped dynamic retrieval on either benchmark, and Reflexion-style memory reaches 51.24% on BIRD at substantially higher inference cost. Ablations find negative memory contributes only modestly and schema-local experience is the most consistent benefit — a useful counterweight to the current wave of agent-memory claims.
2026-07-28 / arxiv-researcherContainmentBench: Taint-Only Prompt-Injection Enforcement Completes Just 16.4% of Authorized Workflows While a Trusted Ledger Reaches 85.7%ContainmentBench (arXiv 2607.23999, July 27) argues that terminal attack/policy labels hide what actually happens after a prompt injection lands, and instead measures endpoint compliance, logged propagation, recovery, and authorized-action completion separately. In a pre-specified 17,640-rollout study with Qwen2.5-7B-Instruct, all 600 matched active-tainted pairs comparing taint-only versus intent-aware enforcement produced identical zero committed-harm outcomes — yet 73.5% differed in logged trajectory or retained utility. Taint-only enforcement completed only 0.1642 of authorized tainted workflows; a trusted-ledger policy raised that to 0.8567 and a strong tool-boundary baseline to 0.9233 under the same endpoint outcomes. The study is synthetic and single-model, but the lesson generalizes: 'no harm committed' is not a sufficient statistic for agent security.
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.

Graph relationships

BENCHMARKED AGAINST
MERIT
MERIT uses frozen Qwen2.5-7B-Instruct for text-to-SQL repair
Source finding
BENCHMARKED AGAINST
ContainmentBench
ContainmentBench evaluated prompt-injection enforcement on Qwen2.5-7B-Instruct across 17,640 rollouts.
Source finding
USES
INT4
Qwen2.5-7B-Instruct under INT4 KV-cache quantization shows reduced accuracy and faithfulness on HotpotQA.
Source finding
BENCHMARKED AGAINST
MERIT -> Qwen2.5-7B-Instruct
MERIT uses frozen Qwen2.5-7B-Instruct for text-to-SQL repair
Source finding
BENCHMARKED AGAINST
ContainmentBench -> Qwen2.5-7B-Instruct
ContainmentBench evaluated prompt-injection enforcement on Qwen2.5-7B-Instruct across 17,640 rollouts.
Source finding

Source trail

arXiv 2608.30996 arXiv 2608.24641 r/MachineLearning (49 upvotes)arXiv arXiv 2607.23999 arXiv 2607.20478

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