The state of bug bounty in 2026
Essay on bug bounty trends in 2026. Discusses AI agent effectiveness for vulnerability discovery and program management challenges.
Essay on bug bounty trends in 2026. Discusses AI agent effectiveness for vulnerability discovery and program management challenges.
Apache 2.0 open standard for governing AI agent payment requests. Policy engine with 12 configurable checks for payment authorization.
Open-source tax software built and maintained by autonomous AI agents. Uses IRS publications as source, applies self-improving agent loops.
Tool for multi-LLM code review consensus. Aggregates feedback from multiple models to identify blind spots and improve code quality assessment.
Essay on LLM-based knowledge management limitations. Discusses problems with AI-generated note synthesis and cognitive organization.
Agent skill implementation for token compression. Reduces output tokens by ~47% while maintaining readability.
Security report on 1.4M AI-driven API test executions. Maps vulnerabilities to OWASP Top 10 using agentic testing.
Cloudflare expands access to OpenAI's frontier models via Agent Cloud platform, enabling enterprises to deploy AI agents for customer support, system updates, and report generation.
Monte Carlo Tree Search approach for multi-attribute controllable summarization without per-attribute fine-tuning, enabling flexible constraint satisfaction.
Co-denoising framework for transferring manipulation skills from human videos to robots by bridging morphological differences.
Security research on defending AI-based videoconferencing systems against pose-expression latent hijacking attacks using biometric detection.
Automated pipeline for scaling reinforcement learning datasets to pretraining scale, addressing data bottleneck in RL for LLM training.
Post-deployment learning framework for Vision-Language-Action policies using retrieved execution memories to improve embodied agent performance.
Data augmentation framework for robotic manipulation using Vision-Language-Action models to improve learning from limited demonstration datasets.
LLM-based framework for predicting flight delays using textual aeronautical information and aircraft trajectory data for air traffic management.
Computational analysis comparing 17,790 articles between Grokipedia (AI-generated) and Wikipedia examining textual and structural biases.
EGMOF: hybrid diffusion-transformer for metal-organic framework generation with inverse design capabilities for materials discovery.
Inference-time optimization using evolutionary algorithms on prompt embeddings for diffusion model control without fine-tuning.
Structured uncertainty framework for LLM agents with tool-calling to generate principled clarifying questions for ambiguous user instructions.
Language-conditioned humanoid robot control using LLM with unified motion vocabulary for free-form command execution and embodied AI.
Bharat Scene Text dataset and benchmark for Indian language scene text recognition addressing script diversity and font variations.
AV-SpeakerBench: multimodal LLM benchmark with 3,212 questions evaluating audiovisual speech understanding and speaker-speech alignment in video.
Analysis of flow-based diffusion models revealing two-stage behavior through oracle velocity field computation and memorization-generalization tradeoffs.
Research on adversarial perturbations for object detectors using black-box attacks to expose vulnerabilities and understand attack mechanisms.
Research on self-distillation methods for teaching language models to leverage cognitive skills like verification and backtracking without base model exposure.
Research on relational visual similarity in computer vision showing how humans perceive analogical relationships beyond attribute similarity.
Framework combining mechanism design and online learning for sequential mechanism design where principal learns agent beliefs while ensuring truthfulness.
Mechanistic study of self-reflection emergence in RL-trained LLMs, proposing two-stage decision-sampling hypothesis to explain unified optimization producing distinct capabilities.
White-box adversarial attack method on computer vision models using SHAP values to generate imperceptible evasion attacks.
Training-free framework for human video animation using cached reference frames to model long-range dependencies while preserving temporal coherence.
Analysis showing layer pruning of LLMs degrades generative reasoning tasks beyond surface degradation, causing loss of algorithmic capabilities.
Method addressing prompt misguidance in diffusion-based super-resolution by using tiled prompts for localized semantic guidance.
Multi-agent framework for smart contract auditing using specialized agents for planning, execution, and recovery with coordination protocols.
Study demonstrating LLM biases when simulating misinformation susceptibility, showing models overstate attitudes and ignore network effects present in humans.
Qualitative study of 33 K12 teachers' perspectives on using conversational AI agents to scaffold group collaboration in classrooms.
Adaptive framework for demand forecasting model selection addressing horizon-induced performance degradation in inventory planning.
Pipeline combining subquadratic retrieval and GPU-accelerated kernels for analyzing immune repertoires at population scale.
Dataset of parasitoid wasps and hymenoptera for taxonomic identification and biodiversity monitoring.
Knowledge distillation method for distilling RL-trained LLMs with chain-of-thought reasoning into smaller student models while preserving reasoning capabilities.
Theoretical analysis explaining why Adam optimizer outperforms SGD through second-moment normalization using stopping-time and martingale analysis.
Analysis showing chain-of-thought prompting underperforms direct answering in medical vision-language models due to perception bottlenecks in domain-specific tasks.
Memory-efficient continual learning method using prototypical exemplar condensation to reduce storage requirements while maintaining performance.
Parallel framework combining imitation and reinforcement learning for autonomous driving, addressing limitations of sequential fine-tuning approaches.
Method to improve pretrained generative robot policies by replacing sampled noise with optimized constant noise vectors for downstream reward optimization.
Mid-training adaptation strategy for LLMs to improve automatic summarization of radiology reports, exploring domain-specific pre-training approaches.
RAM: motion capture system for 3D human pose reconstruction in unconstrained video with occlusion handling and temporal smoothing.
ChronoCon: contrastive learning approach for disease progression assessment from longitudinal medical imaging without explicit severity annotations.
CAIAMAR: multi-agent framework for context-aware image anonymization in street-level imagery using agentic reasoning.
Kill-chain canary methodology for tracking prompt injection attacks across multi-agent LLM systems with stage-level diagnostics.
System for making mathematical theorems interactive by grounding LLM-generated explanations in formal representations enabling execution and stepping.