OrthoEraser: Coupled-Neuron Orthogonal Projection for Concept Erasure
OrthoEraser: concept erasure method for text-to-image models using orthogonal projection to suppress unsafe concepts without collateral damage.
OrthoEraser: concept erasure method for text-to-image models using orthogonal projection to suppress unsafe concepts without collateral damage.
KEPo: poison attack on graph-based retrieval-augmented generation systems targeting knowledge graphs to manipulate LLM outputs.
ReHARK: method for efficient one-shot adaptation of vision-language models using hybrid RBF kernels.
Agentic framework for multimodal query processing with central supervisor coordinating specialized tools across text, image, audio, video, documents.
MANSION: language-driven framework generating multi-floor 3D environments for long-horizon robotic tasks with spatial reasoning.
RoboClaw: agentic framework for long-horizon robotic tasks unifying vision-language-action systems with data collection and policy learning.
Dynamical theory of adaptive coordination in multi-agent systems using recursive feedback architecture.
UtilityMax Prompting: formal mathematical framework for multi-objective LLM optimization using influence diagrams.
Evaluation of seven open-source LLMs for Japanese pathology report writing: text generation, error correction, and clinical usability.
Security analysis of autonomous LLM agents (OpenClaw), identifying attack surfaces across five-layer lifecycle and proposing mitigations.
Training-free token pruning method for efficient 3D medical image processing in vision-language models.
HouseMind: multimodal LLM for understanding, generating, and editing architectural floor plans through tokenization approach.
Proposes simulation-based paradigm for human-agent collaboration with LLMs, enabling users to preview long-term consequences of agent actions before execution.
Policy gradient algorithm improvement preserving entropy and trajectory diversity during language model reasoning training.
Universal semantic evaluation framework for assessing LLM understanding beyond traditional perceptual metrics.
Benchmark for evaluating text-to-video models' ability to capture object state changes specified in prompts.
Compression-Consistency Principle explains why language models prefer correct statements: next-token prediction favors shorter internally consistent data descriptions.
Studies social bandit learning where reinforcement learning agents leverage observing diverse non-expert agents using free energy optimization.
RADAR autonomous closed-loop system for robotic data generation using semantic planning and causal environment reset without human intervention.
Studies emergence of cooperative behavior in hybrid human-agent populations through energy load management game-theoretic scenario.
Systematic review synthesizing 28 secondary studies on generative AI adoption in organizations, identifying technical and organizational challenges.
Identifies Trusted Executor Dilemma: high-privilege LLM agents executing external instructions leak private data through instruction-following vulnerability.
ELISA integrates scGPT expression embeddings with BioBERT for interpretable AI agent discovery in single-cell genomics data.
Mirror design pattern for prompt injection detection using data-curation geometry for fast, deterministic, non-promptable screening.
Bielik-Minitron-7B compresses Bielik-11B model 33.4% via structured pruning and knowledge distillation for European languages.
Online streaming segment-level memory system for multi-turn video reasoning in multimodal LLMs enabling concurrent perception and generation.
EnTransformer deep generative model for multivariate probabilistic time series forecasting with uncertainty quantification using transformers.
Studies whether aligned LLMs refuse to proceed when encountering harmful content within benign tasks, examining content-level ethical behavior.
MobileKernelBench evaluates whether LLMs can generate efficient compute kernels for mobile devices, introducing benchmark dataset for systematic investigation.
Introduces delayed backdoor attacks against pre-trained models where malicious behavior activation is temporally decoupled from trigger exposure.
HomeSafe-Bench: evaluation benchmark for vision-language models detecting unsafe actions in household robot scenarios.
BTZSC benchmark comparing zero-shot text classification methods across cross-encoders, embeddings, rerankers, and instruction-tuned LLMs.
Flowcean: automated data-driven model generation framework for cyber-physical systems focusing on modularity and usability.
Framework for joint machine translation and cross-lingual label projection using XML tags; addresses degraded translation quality in prior combined approaches.
Security analysis of compound AI systems combining LLMs, software tools, and databases; identifies vulnerabilities from traditional software stack and hardware layers.
Slow-Fast Inference: training-free decoding acceleration leveraging stable attention support patterns within semantic spans during autoregressive generation.
Mathematical proof that chemical reaction networks without hidden layers outperform spiking neural networks on certain classification tasks.
LoV3D: vision-language pipeline for cognitive prognosis from longitudinal brain MRI via regional volume assessments and grounded reasoning.
LLM-based neural architecture search with feedback memory: closed-loop pipeline for iterative CNN design on consumer GPU without fine-tuning.
LMP2: browser-based self-audit tool for inspecting LLM associations with individuals, with user study findings on privacy and model behavior.
Minimax deep deterministic policy gradient: RL algorithm for stable performance under external disturbances using fractional objectives.
SommBench: multilingual benchmark assessing LLM capabilities in sommelier expertise, evaluating cultural knowledge beyond linguistic encoding.
Agent-assisted code generation for translating complex RL environments into high-performance implementations with iterative repair and verification.
IsoCompute Playbook: scaling laws for optimal compute allocation in LLM reinforcement learning post-training across rollouts, batches, and update steps.
GlyphBanana: agentic workflow for precise text and formula rendering in generative models, addressing out-of-distribution instruction-following challenges.
Theoretical analysis of catastrophic forgetting in continual post-training of generative models, formalizing mass forgetting and component drift mechanisms.
BehaviorVLM: vision-language framework for animal pose estimation and behavioral understanding without human annotation using finetuning-free approach.
MADQA: benchmark with 2,250 questions over 800 PDFs evaluating whether multimodal agents use strategic reasoning or stochastic search in document-intensive workflows.
LLM-driven system for advancing interdisciplinary scientific research through exploration and reasoning rather than rapid experiment design.
Neural Thickets shows task-specific expert solutions exist near pretrained weights and can be discovered through structured optimization.