3834 papers
This paper proposes a parameter-efficient continual learning framework for Human Activity Recognition that mitigates catastrophic forgetting in domain-incremental scenarios by employing channel-wise gated modulation to adapt frozen pretrained representations through bounded diagonal scaling, thereby achieving superior stability and plasticity with minimal parameter updates.
This paper presents and evaluates five prompt engineering strategies for reducing LLM hallucinations in industrial settings without modifying model weights, finding that an Enhanced Data Registry (M4) achieved perfect consistency in initial trials while a revised Decomposed Model-Agnostic Prompting (M2) showed the most significant improvement in subsequent verification.
This paper introduces eXplicit Sharpness-Aware Minimization (XSAM), a novel and computationally efficient framework that improves upon existing SAM implementations by providing a more faithful interpretation of the ascent gradient and explicitly estimating the direction toward local loss maxima to achieve superior generalization.
This paper introduces \texttt{InFusionLayer}, a novel, open-source Python ensemble tool inspired by Combinatorial Fusion Analysis (CFA) that integrates rank-score characteristic functions and cognitive diversity to enhance multiclassification performance across PyTorch, TensorFlow, and Scikit-learn workflows.
This paper addresses the failure of byte-sequence-based masked modeling in encrypted traffic classification by identifying a mismatch in inductive bias and proposing FlowSem-MAE, a protocol-native tabular masked autoencoder that leverages field-specific semantics and temporal patterns to significantly outperform existing methods with substantially less labeled data.
This paper proposes using Neural Cellular Automata to generate controllable, synthetic non-linguistic data for pre-pre-training large language models, demonstrating that this approach significantly improves downstream performance and convergence speed while outperforming training on much larger natural language datasets.
This paper introduces Agentic AIBOMs, a multi-agent framework that extends static Software Bills of Materials (SBOMs) with autonomous, policy-constrained reasoning to dynamically capture runtime behavior and environmental drift, thereby enhancing supply-chain security through reproducible, context-aware vulnerability assessment and minimal schema extensions to existing standards.
NabaOS is a lightweight, real-time verification framework inspired by Indian epistemology that detects AI agent hallucinations by classifying claims via epistemic sources and cross-referencing them against HMAC-signed tool receipts, achieving high detection accuracy with minimal latency compared to impractical cryptographic proof methods.
This position paper reframes multi-agent memory as a computer architecture challenge by proposing a three-layer hierarchy and identifying critical protocol gaps, with a specific focus on resolving multi-agent memory consistency as the primary obstacle to building reliable and scalable collaborative systems.
This paper introduces the Epistemic Support-Point Filter (ESPF), a mathematically proven optimal evidence-only filter that synthesizes Jaynesian maximum entropy propagation with Popperian falsification updates to minimize worst-case epistemic ignorance, thereby outperforming Bayesian approaches while recovering the Kalman filter in the Gaussian limit.
This paper introduces HTMuon, a heavy-tailed spectral correction method that improves upon the Muon optimizer by preserving parameter interdependencies while inducing heavier-tailed weight spectra, resulting in consistent performance gains in LLM pretraining and image classification alongside theoretical convergence guarantees.
This paper introduces ADVERSA, an automated red-teaming framework that evaluates LLM safety by measuring continuous guardrail degradation and judge reliability across multi-turn interactions, revealing that successful jailbreaks in frontier models often occur early in conversations rather than accumulating over sustained adversarial pressure.
This paper pioneers the application of sparse autoencoders to the Chronos-T5 time series foundation model, revealing a depth-dependent causal hierarchy where mid-encoder features responsible for change detection are more critical to forecasting accuracy than the semantically rich but less causally influential features in the final encoder layer.
This study evaluates 319 LLM-generated patches for 64 Java security vulnerabilities, revealing that while models often preserve functionality, they frequently fail to address security issues due to semantic misunderstandings, resulting in a low overall success rate of 24.8% and highlighting the critical need for rigorous validation before deployment.
This paper demonstrates that neural networks learning a conditional task with K-fold ambiguity first converge to a marginal solution characterized by a log K loss plateau, which persists until an internally assembled selector-routing head triggers a sharp, collective transition to the full conditional solution, a process governed by dataset size and stabilized by gradient noise.
This paper proposes TASER, a decentralized defense framework for UAV swarms that mitigates stealthy backdoor attacks in Federated Learning by leveraging spectral energy refinement to structurally disrupt malicious tasks while preserving main-task accuracy, offering a more efficient alternative to complex outlier detection methods.
The paper introduces "Amnesia," a lightweight adversarial attack that manipulates the internal activation states of open-weight Large Language Models to bypass existing safety mechanisms and induce harmful or antisocial behaviors without requiring any fine-tuning.
This paper introduces Multi-Level Concept Splitting (MLCS) and Deep-HiCEMs to overcome the limitations of shallow hierarchies in concept-based models by automatically discovering multi-level concept structures from coarse annotations and enabling effective interventions at various levels of abstraction.
KernelSkill is a multi-agent framework that enhances GPU kernel optimization by replacing opaque LLM heuristics with a knowledge-driven, dual-level memory architecture of expert skills, achieving state-of-the-art speedups and a 100% success rate on KernelBench.
The paper introduces ES-dLLM, a training-free inference acceleration framework for Diffusion Large Language Models that significantly boosts throughput by dynamically skipping tokens in early layers based on intermediate representation variations and confidence scores, achieving up to a 16.8 speedup while maintaining generation quality.