Introduces AISPA, a user-centric framework to audit system prompts in LLM applications, and applies it to 3,249 instructions from 88 commercial products to classify protective versus problematic instructions. Highlights design variability, growing prompt length/protection, persistent problematic directives, and calls for transparency and oversight.
AI Agent Papers2026
Treats agent self-improvement as natural selection over a population of harnesses (prompts, tools, skills, control flow), evolving a frozen-model agent by selecting harness edits that extend capability without regressing others. Uses a preserve-and-extend contract, lineage archive, and verifier-driven fitness (no gold solutions) to recombine complementary edits and transfer across benchmarks.
AI Video2026
Turns a short prompt plus aspect ratio and duration into a structured, shot-by-shot audio-video description for text-to-audio-video generation. A PEFT LoRA on Qwen3.6-27B that expands timing, camera motion, continuity, and synchronized diegetic/non‑diegetic sound; text-only and requires MiniMax-H3 + LightX2V to produce final AV.
Chatbot2026
Provides a drop-in Jinja chat template for Qwen 3.5/3.6/3.8 that reduces reasoning-token waste, enforces a concise terseness system prompt, and preserves in-chat reasoning and tool-call rendering across turns. Terseness is on by default but switchable per request; no model weights are changed.
AI Agent Papers2026
Sungho Park, Wonjoong Kim +11Affiliation: KAIST, Affiliation: Southern University of Science and Technology +1
Automatically optimizes runtime harnesses for LLM agents by diagnosing failure traces and iteratively applying structured, generalizable patches. Combines batch-based failure diagnosis, code-like patch generation across prompts/tools/middleware, and validation-aware selection to raise long-horizon task success on multiple benchmarks.
AI Dataset2026
Provides an unattended text-to-video-and-audio streaming toolkit built around FastH3 (a 4-step distillation of MiniMax-H3): generation/retime/HTTP push scripts, a 221-scene prompt library, checkpoint conversion and ComfyUI workflows to run a continuous local stream.
AI Agent Papers2026
Synthesizes a deployment-specific safety harness (natural-language policy plus executable logic) for a frozen LLM in a target domain, using model behavior, domain specs, and adversarial review to optimize safety–utility trade-offs and transfer across agent benchmarks.
Enables specifying exact 24-bit hex colors for object-level image generation and editing via a unified prompt interface, using a Paint-500K dataset and timestep-gated pure-color anchors to improve color fidelity.
AI Video Papers2026
Transforms user prompts into shot-level cinematic directions for text-to-video generation, using a 397B prompt-enhancer trained on 1.05M videos and SC-GRPO to preserve semantic consistency across shots; evaluated on WanPEval (5–30s) with large human-preference gains.
AI Agent Papers2026
Alternates a Planner (issues sub-queries) and a Synthesizer (integrates retrieved evidence into a persistent summary) to tackle long-horizon deep-search; introduces Role‑Decoupled Policy Optimization (RDPO) for role-specific RL credit assignment and shows strong results (IterSynth-8B reaches 50.7% on five benchmarks).
Assesses how AI reviewers react to content-preserving rewrites and introduces RobustReview, a controlled benchmark plus SciCore, a dual-branch reviewer that combines full-manuscript judgment with a structured 'science core' summary to reduce rhetorical sensitivity.
AI Image2026
Generates images from natural-language prompts or structured JSON layouts, fine-tuned from a 5B backbone to improve bilingual long-text rendering and layout control; runs via a Diffusers pipeline and expects PyTorch + an NVIDIA GPU.