Prompt engineering is the practice of crafting and structuring instructions given to a language model to obtain accurate and useful outputs. For example, chain-of-thought prompting techniques measurably improve GPT-4's performance on reasoning tasks.
ToolGate is a lightweight controller that decides whether to execute or skip tool calls proposed by vision-language agents. Across five benchmarks with Qwen3-VL, it reduces token cost to 64-69% of the ReAct baseline while preserving accuracy, and further improves accuracy by 1.65 points with matched-domain training.
Method to extract reasoning primitives from ReAct agent traces. Recurrent reasoning moves are clustered and converted into typed pseudo-tools. Induced libraries outperform the source agent: +44pp on RuleArena NBA, +30pp on MuSR, +22pp on NatPlan.
Context-mode optimizes context window for AI coding agents by sandboxing tool outputs. Achieves 98% token reduction. Compatible with 15 platforms.
DOPA, a demonstration retrieval framework, uses an OOD proxy to approximate the inaccessible target domain and guide selection of relevant demonstrations. A Mahalanobis distance-based global diversity constraint ensures sufficient variety among retrieved examples. Positive results across multiple LLMs and tasks under severe distribution shift.
Humor generation system for SemEval-2026 Task-1 (MWAHAHA) using a "generate-many → select-best" strategy. Diverse candidate generation via multi-step prompting and model ensembling, then selection using a preference model trained on 2.5K human pairwise judgments. Ranked 1st in English and Chinese subtasks, 2nd in Spanish.
Empirical study adapting Phi Silica for short-form text rewriting via dataset curation, prompt distillation, and parameter-efficient fine-tuning. Dataset built from public slide decks. Results: improved semantic fidelity, reduced hallucinations, higher preference win rate vs GPT-5-chat.
arXiv study on LLM adaptation limits for annotation tasks. Toxicity detection experiments across diverse datasets show 66% of zero-shot errors resist correction via prompting (rescue rate 34.8%). Models follow misaligned definitions while maintaining confidence. Definition-Specific Familiarity (DSF) metric correlates with performance (r=+0.41), outperforming memorization metrics.
Demo2Reward optimizes VLM reward model language instructions at test-time using 3-10 expert demonstrations to reduce false positives in robotics. No additional training required. Validated on simulated tasks and real-world transfer.
FETCH, an automated legal triage classifier, generates follow-up questions using a low-cost LLM ensemble. The study shows cheap models perform well at classification, but high-quality plain-language question generation requires GPT-4 or higher. Prompt engineering alone is insufficient; LLM-as-judge ratings diverge from human evaluations.
KACE decouples storage from usage in context for mathematical reasoning. An epistemic tree stratified by difficulty and domain is built offline via self-reflective loop. At evaluation, tiered self-consistency dynamically classifies problems and selectively retrieves matching cards. On AIME 2025: 62.2% accuracy (+10.4 points vs Best-of-5).
Researchers demonstrate that hidden reasoning traces in LLMs can be extracted via Reasoning Exposure Prompting (REP), a lightweight prompting method using shadow-model-generated demonstrations in auxiliary code-like formats. REP exposes internal traces even when deployed systems intentionally hide them, while preserving useful reasoning signals for distillation.
Hackers exploited Meta's AI support chatbot to gain access to high-profile Instagram accounts. By simply asking the bot to link a new email address to a target account, they bypassed the entire account recovery process. Meta had wired its support system to an AI capable of executing account changes in a single request.
Developer builds LIA, a persistent cognitive runtime around an LLM without behavioral prompts. Architecture includes 20k+ self-evaluated memories, cognitive kernel (LCRK v3), self-rule system, and private Linux domain. Control test: standard LLM in identical ecosystem remains inactive.
Impeccable is a design language to improve AI tools' ability to generate interfaces. The GitHub project offers a structured approach to guide models in creating coherent designs.
Study on harness self-evolution (prompts, skills, memories, tools) in LLM agents. Analyzes two capabilities: harness-updating (producing useful updates) and harness-benefit (benefiting from them). Findings: harness-updating is capability-agnostic (Qwen3.5-9B matches Claude Opus gains), while harness-benefit is non-monotonic (mid-tier models benefit most).
MASA (Model-Aware Skill Alignment) adapts procedural skills for LLM agents to each model backbone without weight modification. A hierarchical evolution pipeline rewrites skills via hill climbing and UCB-driven tree search, then a lightweight rewriter trained on trajectories reproduces adaptation in a single forward pass. Gains up to 25.8 points across three interactive environments and four backbones.
arXiv study on iterative refinement of LLM-generated reward functions for sparse structured RL. Authors identify two dominant failure modes (reward flooding, semantic misunderstanding) and propose diagnostic-driven refinement guided by failure-mode taxonomy. Results: DoorKey-8x8 improves from 2.3% to 97.6%, KeyCorridor from 31.2% to 86.7%. Limitations: method restricted to PPO and sparse structured tasks.
arXiv study on LLM security against untrusted inputs. Researchers test whether wrapping untrusted content in mock tool calls improves robustness across 7 models and 3 LLM-as-a-Judge tasks. Finding: the approach fails and typically increases attack success rates, inverting the expected instruction hierarchy.
COLLEAGUE.SKILL is an automated trace-to-skill distillation system for generating person-grounded AI skills via expert knowledge extraction. The system produces versioned packages with two coordinated tracks: capability (practices, mental models, decision heuristics) and bounded behavior (communication style, interaction rules). 18.5k GitHub stars, 215 skills from 165 contributors.
A r/LocalLLaMA user built an autonomous agent with Qwen 3.5 27B enhanced by short/long-term memory (memory.md file, daily summaries, self-reflections). The agent handles complex tasks (app creation, web search, software installation). User prefers this local setup over GPT/Gemini for UX despite lower raw capability.
Promptloop is a terminal tool to create, run, and improve prompt evaluations. Enables rapid iteration on prompt quality without leaving the CLI.
StorySparkAI is an open-source platform enabling users to generate and share multiple story variations from a single prompt. Designed for creative professionals.
A developer embedded a malicious prompt injection into code shared with 'vibe coders' to trigger data deletion. The incident highlights security risks from prompt injections in development workflows.
A developer experiments with HTML as the primary chat language for coding agents instead of markdown. By switching the system prompt to HTML, the agent (Qwen 3.6-27B) now generates SVG diagrams directly in responses. Results are promising but the model still tends to default to markdown.
Study on LLM reward design failures in sparse structured RL. Authors identify two dominant failure modes (reward flooding, semantic misunderstanding) and propose diagnostic-driven iterative refinement. On MiniGrid, DoorKey-8x8 improves from 2.3% to 97.6% success; KeyCorridor from 31.2% to 86.7%. Failure-mode taxonomy is the primary mechanism.
Thoughts-as-Planning formalizes reasoning chain optimization as sequential decision-making over latent semantic space. The framework learns a latent world model simulating effects of reasoning chain edits on outputs, supporting multi-scale edits (token, segment, instruction) via gradient descent or reinforcement learning planning.
Paper introduces Program-of-Thoughts prompting for chart summarization: VLMs generate Python programs to derive valid summary statistics instead of direct text. Proposes chart-to-dictionary auxiliary task. Results match existing methods on semantic and factual metrics.
Study of persona effects on explanations generated by multimodal LLM agents in urban perception. Analysis of 59,808 annotations from 1,200 persona-conditioned agents: captions show strong convergence, justifications display systematic variation tied to socioeconomic and political attributes, perception tags show no significant persona-related differences.
Study on prompt tone impact on LLM performance. Tests on ChatGPT-4o, ChatGPT-5-nano, Gemini 2.5 Flash/Lite using 50 base questions and 570 MMLU questions (57 subjects) in 5-7 tone variants. Results: tonal effects are systematic but highly model-dependent, with significant accuracy variations across subjects.
Study of chain-of-thought (CoT) transfer across models using a provider-receiver framework. Full traces often transfer successfully, but mechanisms vary: answer extraction (AIME), receiver competence (MMLU-Pro), or partial structured information (ZebraLogic). In free-generation mode, partial CoTs improve performance, suggesting guidance for continued reasoning.
eXTC combines structured prompt optimization and reinforcement learning for text classification. The system learns a natural language rulebook first, then distills reasoning from a teacher LLM into a compact model, then expands capabilities via RL. Result: fast inference with local reasoning traces and global modular explanations of learned domain rules.
Comparative test of Qwen 3.6 35B across output formats (raw text, markdown, HTML, HTML+CSS). Markdown achieves best quality (78/100 per ChatGPT-4o) with 1,496 output tokens in 23s. HTML+CSS generates 10,290 tokens in 82s but lower quality score (58/100). Measurements include reasoning tokens, throughput, and total time.
SkillGrad optimizes LLM agent skills using a gradient-descent-inspired framework. Task executions provide trajectory-level loss signals, automatic diagnostics generate text-based gradients, and a momentum agent accumulates recurring patterns. Evaluated on SpreadsheetBench and WikiTableQuestions, SkillGrad outperforms training-based baselines by 6.7 percentage points on average.
TCP-MCP co-evolves agent prompts and communication topologies as a unified genome. On MMLU-Pro, MMLU, and GSM8K with DeepSeek-V3.2 backbone, the system achieves 82.66%, 89.96%, and 96.61% accuracy while consuming 5.69× fewer tokens than debate-style systems.
Method to translate soft prompts into natural language prompts using a dedicated translation model. Translations outperform InSPEcT across multiple benchmarks. Application: soft prompts optimized on small open-source models convert to portable text prompts that exceed original performance when deployed on closed-API models.
UniMaia is a framework controlling a chess policy (Lc0) via natural language prompts without full multimodal retraining. A lightweight text encoder and ControlNet-style mechanism enable gameplay modulation (opening selection, strength). UniMaia-Aux adds temporal and behavioral prediction objectives. SOTA results on prompt-conditioned benchmarks.
LaneRoPE enables multiple sequences generated in parallel to collaborate during inference. The method adds inter-sequence attention masking and extends RoPE to capture relative token positions within and across sequences. Tests on mathematical reasoning show accuracy gains with negligible overhead.
Hierarchical framework for compact LLMs in resource-constrained agentic systems. Model distillation + oracle-controller loop monitors protocol validity, projects histories into feasible prompt domain, triggers lightweight fine-tuning under drift. Separates schema learning from semantic adaptation. Evaluated on Multi-Fidelity Bayesian Optimization with improved reliability and cost-efficiency.
WIRE is an evaluation pipeline that diagnoses rule conflicts within a single LLM agent prompt policy. Across 6 public policies, it extracts 276 rules and identifies 170 hard-collision rule pairs. Only 35.4% of tested cases comply with both rules simultaneously; 64.6% violate at least one source rule.
Fine-grained style control technique for prompt-based TTS models. Inter-utterance interpolation using direction vectors in embedding space (99-100% gender conversion success, 36 Hz pitch variation). Intra-utterance transitions via KV-cache swapping and sliding-window attention masking (speaker similarity 0.81-0.91).