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Parameter Estimation for Unnormalized Discrete Models via Empirically Localized Deformed Bregman Divergence

model-releasesarxiv-cs-lg

arXiv:2609.30713v1 Announce Type: cross Abstract: Estimation of parameter of probabilistic models is an important task in the field of machine learning.For models of discrete variables, calculation of

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Parameters vs. Context: TRACE Fine-Tuning for Robust Retrieval-Augmented Generation

DGX agent

arXiv:2609.30337v1 Announce Type: new Abstract: Retrieval-Augmented Generation (RAG) mitigates knowledge obsolescence and factual hallucination in large language models by introducing external context

agentsarxiv-cs-lg

28 Sept 2026

Peer-Grounded Counterfactual Path Planning for Chronic Health Management

arXiv:2609.30838v1 Announce Type: new Abstract: Effective behavioral intervention in chronic disease management requires not a single prescription but a sequence of incremental steps, each grounded in

Peer Review as Structured Commentary: Immutable Identity, Public Dialogue, and Reproducible Scholarship

arXiv:2506.22497v2 Announce Type: replace-cross Abstract: This paper reconceptualises peer review as structured public commentary. Traditional academic validation is hindered by anonymity, latency, an

Persistent Homology of Time Series through Complex Networks

arXiv:2605.01624v1 Announce Type: cross Abstract: We present a unified pipeline for univariate time series classification via complex networks and persistent homology. A time series is mapped to a gra

Persistent Negatives for Adversarial Black-Box On-Policy Distillation

arXiv:2609.30864v1 Announce Type: cross Abstract: Black-box On-Policy Distillation (OPD) seeks to improve a student from its own generations when the teacher provides sampled responses but not token p

PHASE: Compliance-Enabled Tactile Phase Retrieval for Few-Shot Insertion Learning

arXiv:2609.30889v1 Announce Type: new Abstract: Contact-rich assembly tasks such as peg-in-hole insertion remain difficult to learn from limited demonstrations. While retrieval-augmented imitation lea

Applications

PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution

arXiv:2609.30988v1 Announce Type: new Abstract: Real-world image super-resolution (SR) requires recovering perceptually realistic high-resolution images from complex low-resolution observations while

applicationsarxiv-cs-cv

PhysElite: How Far Are LLMs from Solving Olympiad-Level Physics Problems?

arXiv:2608.25097v2 Announce Type: replace Abstract: Understanding how (multimodal) large language models perform on physics problems requires benchmarks that reflect the difficulty and breadth of expe

Agents

PIA: A Personal Intelligence Agent Turning Health Conversations into Records and Records into Understanding

arXiv:2609.31255v1 Announce Type: new Abstract: General-purpose agent memory summarizes conversations: it extracts salient snippets, embeds them, and retrieves the top-k into the prompt. A health agen

agentsarxiv-cs-cl

PICO: Projection-Informed Consistency Optimisation for 6DoF Surgical Tool Pose Estimation

arXiv:2609.30989v1 Announce Type: new Abstract: Purpose: Accurate 6 DoF pose estimation of surgical tools is critical for automa- tion, robotic proprioception, and safe interaction with the tissue ope

PixSim: a calibrated open-source simulator of instant-payment fraud, recovery and interdiction under analyst capacity constraints

model-releasesarxiv-cs-lg

arXiv:2609.30684v1 Announce Type: new Abstract: Brazil's Pix settles about 5.9 billion instant, irreversible transfers a month. A fraudulent transfer can be recovered only while the funds remain in a

Local Ai

Pocket-STVG: lightweight architecture for Spatio-Temporal Video Grounding

arXiv:2609.31135v1 Announce Type: cross Abstract: Spatio-Temporal Video Grounding (STVG) aims to localize the spatio-temporal tube in a video corresponding to a natural language query. While recent me

local-aiarxiv-cs-ai

POIL: Point-based One-Shot Imitation Learning with Stable Dynamical Systems

arXiv:2609.30404v1 Announce Type: new Abstract: We present POIL, a point-based one-shot imitation learning framework with stable dynamical systems. While one-shot imitation avoids collecting extensive

researcharxiv-cs-ro

Policy-Calibrated DAgger: Offline Calibrated Noise Injection for Imitation Learning

arXiv:2609.30462v1 Announce Type: new Abstract: Policies trained with imitation learning can accumulate errors over time, causing the robot to drift outside the training distribution. Existing methods

Policy Regret for Embedding Model Routing: Contextual Bandits with Low-Rank Experts

arXiv:2606.14929v2 Announce Type: replace-cross Abstract: Modern recommendation systems increasingly rely on dynamically routing diverse queries to multiple embedding models. Despite its practical sig

PolicyAttention: Softmax Attention Implements Policy Mirror Descent for Closed-Loop Control

arXiv:2609.30500v1 Announce Type: cross Abstract: Can causal softmax attention implement policy mirror descent as a repeated controller rather than a one-step algebraic identity? Negative-entropy poli

Population loss in shallow ReLU networks: Bias & families of critical points

arXiv:2609.30661v1 Announce Type: new Abstract: The main result presented is a formula for the population loss in the student-teacher kernel model that is applicable to shallow ReLU networks with bias

safetyarxiv-cs-lg

PORL: Pretrained Offline Reinforcement Learning for the Job Shop Scheduling Problem

arXiv:2609.30948v1 Announce Type: cross Abstract: The Job Shop Scheduling Problem (JSSP) is a fundamental combinatorial optimization problem in industrial optimization. This work introduces Pretrained

Praxis: Distilling Physical Interaction Priors from Egocentric Videos for Generalizable Whole-Body Manipulation

arXiv:2609.30735v1 Announce Type: new Abstract: Mobile humanoid manipulation requires both reaching a usable workspace and preserving precise hand-object interactions as object poses and contact condi

Applications

Precision at Speed: Sample-Efficient Online Model-Based Reinforcement Learning for Hydraulic Excavator Control

arXiv:2609.31025v1 Announce Type: cross Abstract: Precise, high-speed control remains challenging for robots with complex actuation dynamics. Learning directly on hardware is further constrained by th

applicationsarxiv-cs-lg

Predicting Transmembrane Protein Topology from 3D Structure

arXiv:2609.30446v1 Announce Type: new Abstract: This paper presents a novel approach to infer protein topology using the state-of-the-art graph neural network (GNN), SchNet. The model is trained on th

Preference-based opponent shaping in differentiable games

arXiv:2412.03072v2 Announce Type: replace Abstract: Strategy learning in game environments with multi-agent is a challenging problem. Since each agent's reward is determined by the joint strategy, a g

Preserve-and-Compose Training for Composed Image Retrieval

arXiv:2609.31202v1 Announce Type: new Abstract: Composed image retrieval (CIR) aims to retrieve images that satisfy a user-specified modification while preserving relevant visual content from a refere

Pretrained ASR Pseudo-labeling for Noisy Police Audio

arXiv:2609.30469v1 Announce Type: new Abstract: Pretrained ASR systems perform poorly on noisy Broadcast Police Communication (BPC), hindering efforts to understand police decision-making. Pseudo-labe

PriceBench: A Diagnostic Benchmark for Price, Quality, and Brand Preferences in LLM Booking Agents

arXiv:2609.31468v1 Announce Type: cross Abstract: LLMs increasingly act as purchasing agents, which makes the LLM, not the user, the one choosing among the options that satisfy a request; its preferen

Privacy-Preserving Prompted Policy Search for Robotic Control

arXiv:2609.30554v1 Announce Type: new Abstract: Large language models (LLMs) have recently demonstrated promising capabilities as in-context policy optimizers for Reinforcement Learning (RL), enabling

Tutorials

Probabilistic Robustness-driven Universal Adversarial Perturbations with Explainability against Deep Reinforcement Learning-based Intrusion Detection System

arXiv:2609.30605v1 Announce Type: new Abstract: Deep reinforcement learning (DRL) enables adaptive intrusion detection in dynamic network environments but also exposes intrusion detection systems (IDS

tutorialsarxiv-cs-lg

Agents

Probing Stability-Plasticity Tradeoffs in Agent Memory through Cognitive Experimental Paradigms

arXiv:2609.30558v1 Announce Type: cross Abstract: Agent memory systems are increasingly used to maintain long-term user preferences, task states and evolving facts, but current evaluations often colla

agentsarxiv-cs-ai

ProCAP: Probabilistic Cross-Attentive Prompt Learning for Vision-Language Models

arXiv:2609.30434v1 Announce Type: new Abstract: Pre-trained vision-language models such as CLIP can recognize new categories via prompting, but they often struggle when labeled data are scarce or the

Programs-of-Layers in LLMs through the Lens of Cortical Areas

arXiv:2609.31360v1 Announce Type: new Abstract: Inference in LLMs is conventionally a fixed-depth, fixed-order forward pass through every layer, regardless of how difficult the input is. The human bra

Progressive Memory Transformer: Memory-Aware Attention for Time-Series

arXiv:2609.31351v1 Announce Type: new Abstract: Time-series carry structure simultaneously at multiple scales (fine-grained variation, mid-range motifs, and global properties) and downstream tasks ope

Prompt-Based Continual Compositional Zero-Shot Learning

arXiv:2512.09172v3 Announce Type: replace-cross Abstract: We tackle continual adaptation of vision-language models to new attributes, objects, and their compositions in Compositional Zero-Shot Learnin

Prompt Injection Detection for Email Agents Through Attack Chain Modeling

arXiv:2609.30657v1 Announce Type: cross Abstract: Large language model email assistants are particularly vulnerable to indirect prompt injection because untrusted email content can be retrieved into t

safetyarxiv-cs-cl

Prompt Minimization: Reducing Input Redundancy Without Sacrificing Output Fidelity

arXiv:2609.31505v1 Announce Type: new Abstract: Despite the growing capabilities of large language models (LLMs), prompt design remains largely heuristic and ad hoc. This project will explore extit{pr

Proper Scoring Rules for Right-Censored Survival Data

arXiv:2606.06393v2 Announce Type: replace Abstract: Proper scoring rules provide a rigorous theoretical basis for the training and evaluation of probabilistic forecasts. In survival analysis, such for

Proportional Representation in Temporal Voting with Ranked Preferences

arXiv:2609.30555v1 Announce Type: cross Abstract: We study proportional representation in temporal voting, where one candidate is selected in each round. While prior work has focused on approval ballo

Applications

Provable Speech Attributes Conversion via Latent Independence

arXiv:2510.05191v3 Announce Type: replace-cross Abstract: Conditional generation and disentangled representation learning are central to controlled generation across audio, vision, and multimodal doma

applicationsarxiv-cs-ai

Pseudo-Invertible Neural Networks

arXiv:2602.06042v2 Announce Type: replace-cross Abstract: The Moore-Penrose Pseudo-inverse (PInv) serves as the fundamental solution for linear systems. In this paper, we propose a natural generalizat

PTC-Decoder: Towards Intelligent SLMs on Offline Resource-Constrained Edge Devices

arXiv:2609.30836v1 Announce Type: new Abstract: Deploying small language models (SLMs) on offline, resource-constrained edge devices such as remote sensing satellites presents a fundamental challenge:

Purin: A Biology-inspired Mechanism for Artificial Neural Networks

arXiv:2609.31235v1 Announce Type: new Abstract: Artificial neural networks (ANNs) usually represent neural transmission with fixed trainable weights during a training batch, which omits short-term cha

QSV: Quat-Sphere-Vision for Coupled Quaternion Attention on Spherical Lattices

model-releasesarxiv-cs-cv

arXiv:2609.30592v1 Announce Type: cross Abstract: In standard attention, three separately learned projections decide how strongly a token attends to each neighbor (W_Q, W_K) and how the attended featu

Quadruped Obstacle Avoidance and Footstep Planning with Distributed Low-cost Time-of-Flight Sensors

arXiv:2609.31008v1 Announce Type: new Abstract: Quadruped robots typically rely on depth cameras and LiDAR sensors to map their local environment. However, these sensors have limited close-range cover

researcharxiv-cs-ro

Quantizing Looped Transformers: Feedback Exposure and Calibration Blindness

arXiv:2609.30820v1 Announce Type: cross Abstract: Looped transformers reuse weights across recurrence steps, making low-bit quantization especially attractive. We identify two distinct failure modes o

researcharxiv-cs-cl

Quantum Diffusion Models for Medical Image Analysis

arXiv:2609.31070v1 Announce Type: cross Abstract: Quantum Machine Learning is a novel field of research aimed at devising machine learning approaches exploiting principles of quantum mechanics, such a

Query-Conditioned Prototype Adaptation for Cross-Domain Few-Shot Learning: Single-Query Inference, Controlled Comparisons, and Failure Modes

model-releasesarxiv-cs-cv

arXiv:2609.30769v1 Announce Type: new Abstract: Cross-domain few-shot learning requires adapting a classifier to a new visual domain from very few labelled examples without target-time parameter updat

Rank-Reliable Teacher-Guided Fitness Approximation for Expensive Evolutionary Optimization: A TinyML Architecture Search Study

arXiv:2609.30553v1 Announce Type: new Abstract: Expensive evolutionary search does not always need an exact fitness estimate for every candidate. It often needs a reliable answer to a simpler question

RAPTOR: Ridge-Adaptive Logistic Probes

arXiv:2602.00158v3 Announce Type: replace-cross Abstract: Probing studies what information is encoded in a frozen LLM's layer representations by training a lightweight predictor on top of them. Beyond