arXiv subjects

Junxi Huang

Publications and source records attributed to Junxi Huang.

8 recordsLinked to original sources

arXiv2026

Behavioral Affinity Transduction for Zero-Shot Vision-Language Classification

An image that is hard to classify on its own may have a helpful neighbor in the same unlabeled batch. Zero-shot vision-language models (VLMs) score images independently and leave this evidence unused. We study small unlabeled query batches with no labeled examples. Visual similarity can reflect pose or background, so it may connect images from different classes. Each image's scores over the class vocabulary offer another way to choose neighbors. We introduce Behavioral Affinity Transduction (BAT), which builds a sparse graph from these class-response profiles. The graph supplies neighbor evidence during prediction. For EM-Dirichlet, it also screens candidate classes and initializes assignments. BAT plugs into direct and iterative readouts of a frozen VLM. Across diverse datasets, BAT improves the mean accuracy of every tested readout. In the main RN50 comparison, soft and hard EM-Dirichlet gain $4.22$ and $2.72$ percentage points.

cs.CV↗

arXiv2026

RayAR: Autoregressive Dominant Path Generation for Low-Overhead MIMO Beam Alignment

Beam alignment in multiple-input multiple-output (MIMO) systems requires selecting suitable transmit and receive beam pairs with limited measurement overhead. Although 3GPP Release 18 investigates beam prediction from partial measurements, collecting these measurements still incurs probing overhead. Geometry can focus this probing through the departure and arrival directions of dominant paths, whose identification requires accounting for coupled diffraction interactions. To address this challenge, we propose RayAR, an autoregressive method that generates dominant propagation paths from urban geometry. RayAR conditions candidate ranking on the path prefix, local diffraction geometry, and receiver reachability, updating this context after each selection until the receiver becomes visible. The completed path's departure and arrival directions rank beam pairs for probing, allowing the pair with the highest received power to be selected from a small candidate set. Under 3GPP Release 18 beam-management metrics, RayAR reduces probing by 96.88\% relative to exhaustive sweeping and achieves the highest optimal-pair hit rate among compared methods at the same budget. Real-world measurements further demonstrate the accuracy of this geometric guidance, with simulation-trained RayAR achieving the lowest departure and arrival angular errors among compared methods without fine-tuning and a mean arrival-direction error of 9.07$^\circ$.

eess.SP↗

arXiv2026

RFLocMatch: Reference-Conditioned Residual Learning from Angle-Delay Spectra for Accurate Localization

Radio fingerprint localization supports mobile robots and location-aware services when satellite positioning is unreliable. Angle-delay power fingerprints capture power distributions over angles and delays, but localization remains vulnerable to spatial ambiguity and measurement perturbations. Reflections and blockage can produce similar fingerprints at distant locations, while noise in query fingerprints can alter query-reference rankings despite a clean reference library. These effects can place a distant reference first in the ranking, creating tail errors. Similarity scores can generate plausible candidates, but reliable selection requires candidate-specific residual evidence and comparison across alternatives. This separation motivates RFLocMatch, which uses complementary joint and link-specific matching to preserve candidate locations supported by different links. Reference-conditioned residual learning then scores each candidate using the query and reference fingerprints, their absolute difference, and their element-wise product, while candidate-context modeling compares alternatives. Tail-focused training emphasizes costly errors, and inference replaces the initial match only when a candidate's score advantage exceeds a validation-calibrated threshold. On ray-tracing-generated data, RFLocMatch reduces dual-transmitter 99th-percentile error by 75.2% under link and receiver perturbations relative to the best-performing baseline. On real-world single-transmitter channel impulse response (CIR) measurements with motion-assisted angular-spectrum estimation, it attains 8.25-m 90th-percentile error and 3.83-m mean absolute error.

eess.SP↗

arXiv2026

Map2APS: A Physically Grounded Benchmark for Direct Angle Power Spectrum Prediction from Urban Geometry

Angle power spectrum (APS) characterizes the directional distribution of received signal power and is directly relevant to beam management and MIMO processing. While environment-aware learning has been widely studied for radio maps and path loss, direct map-to-APS prediction still lacks a standardized large-scale benchmark. This paper presents Map2APS, a physically grounded benchmark constructed from intelligent ray-tracing (IRT) path-level propagation records. Map2APS covers 51 equal-height urban maps and approximately 2.55 million Tx--Rx samples, with a strict cross-map split for evaluating generalization to unseen urban layouts. We benchmark representative model families and introduce MS-AReg as a strong reference baseline. On the full held-out test set of 249{,}993 samples, MS-AReg achieves a cosine similarity of 0.948, a peak location error of 1.20$^\circ$, and an inference latency of 0.101 ms/sample. We further report dominant-direction metrics, including Top-1 dominant peak hit rate and dominant peak recall, to evaluate whether predicted spectra preserve decision-relevant arrival directions. The benchmark, code, and evaluation scripts are released at https://github.com/UNIC-Lab/aps-data.

eess.SP↗

arXiv2026

RelLedger: Relational Information Governance for LLM Agent Organizations

In LLM agent organizations, shared records determine which facts are available for later decisions. A sender consults interaction histories before deciding whether to report an event. A delivery gap across histories does not show whose relationship drove the choice or whether the resulting record matters downstream. We introduce RelLedger to audit these links. It holds the event, evidence, risk, and message fixed while crossing the sender's histories with the event target and an unrelated actor. Role reassignment and history rewrites locate the controlling cue. Message and cost interventions show when the effect appears. An attribution ledger maps each claim to its required response cells and separates missing interventions from sampling uncertainty. Recipient replay follows delivered information into downstream decisions. Executable workflows for a simulated one-person company extend the audit to retrieved histories, tool calls, and shared ledger updates. Across GPT, Gemini, Claude, DeepSeek, and Qwen, the audit reveals model-specific control paths and smaller relation gaps under explicit loss rules. Claude sends the same factual notice 62.22 percentage points more often with conflict histories than with friendly histories. In executable workflows, strained histories reduce recipient review rates by 45.83 points relative to warm histories for Claude.

cs.MA↗

arXiv2026

RadioDiff-v2: Generative Angular Radio Maps for Multi-Beam Selection and Localization

Angular radio maps describe the received-power distribution over the angle of arrival and underpin beam selection and receiver localization in sixth-generation (6G) networks. Predicting the angular power spectrum (APS) from geometry is difficult, because the mapping is ill-posed in non-line-of-sight (NLOS) conditions and must generalize to unseen environments. Distortion-minimizing regressors return the conditional mean, which over-smooths the spectrum and erases the multipath structure that downstream tasks need. We cast the task as a perception-distortion problem and propose RadioDiff-v2, a dual-branch one-dimensional diffusion transformer trained with flow matching. It couples periodic angular encoding, adaptive layer-normalization conditioning, a Fourier angular mixer, and joint velocity and clean-signal heads. A per-metric estimator portfolio reads every deployment quantity from this single model, so that samples carry the distribution, the clean-signal head supplies a regression-grade point estimate, Bayes-optimal rules select beams, and the conditional likelihood localizes the receiver. We prove that a concentrated conditional yields a straight probability-flow trajectory that one step integrates exactly, identifying deterministic transport as the correct inductive bias. On a zero-shot test of 99 environments and one million links, RadioDiff-v2 leads every baseline on every metric, with a 0.39 dB Wasserstein-1 distance, per-bin error below the regression baseline, a 2.43 dB eight-beam NLOS sweep loss, and a 20.6-pixel localization error with four base stations. Code is available at https://github.com/UNIC-Lab/RadioDiff-v2.

cs.IT↗

arXiv2026

Dual-Selective Network for Domain-Incremental Change Detection

Domain-incremental change detection (DICD) continuously adapts models to new geographic domains while preserving prior knowledge. However, a structural mismatch exists: the label space remains fixed while domain characteristics vary drastically. Consequently, incremental models struggle to maintain stable spatial change representations across domains. Existing strategies, such as replay-based or regularization-based methods, often fail to scale to long domain sequences, leading to knowledge degradation or increased computational cost. We propose Dual-Selective Incremental Network (DSINet), a unified framework built on visual state space models. DSINet leverages Mamba's input-dependent selective mechanism through a selective spatial state unit (S3U). This unit preserves stable spatial change structures while filtering domain-specific variations during feature propagation. As a result, spatial representations remain stable across domains, preventing the accumulation of feature confusion over incremental steps. Additionally, we employ a concentration-balanced distillation (CBD) strategy to stabilize knowledge transfer across domains. It balances hardness and confidence concentration effects during incremental updates. This ensures reliable probability mass allocation and prevents over-smoothing or mode collapse during distillation. Together, these mechanisms maintain stable learning dynamics throughout incremental stages. Experimental results demonstrate that DSINet mitigates knowledge degradation across long domain sequences while maintaining the linear computational efficiency of state space models.

cs.CV↗

arXiv2026

Physics-informed line-of-sight learning for scalable deterministic channel modeling

Deterministic channel modeling maps a physical environment to its site-specific electromagnetic response. Ray tracing produces complete multi-dimensional channel information but remains prohibitively expensive for area-wide deployment. We identify line-of-sight (LoS) region determination as the dominant bottleneck. To address this, we propose D$^2$LoS, a physics-informed neural network that reformulates dense pixel-level LoS prediction into sparse vertex-level visibility classification and projection point regression, avoiding the spectral bias at sharp boundaries. A geometric post-processing step enforces hard physical constraints, yielding exact piecewise-linear boundaries. Because LoS computation depends only on building geometry, cross-band channel information is obtained by updating material parameters without retraining. We also construct RayVerse-100, a ray-level dataset spanning 100 urban scenarios with per-ray complex gain, angle, delay, and geometric trajectory. Evaluated against rigorous ray tracing ground truth, D$^2$LoS achieves 3.28~dB mean absolute error in received power, 4.65$^\circ$ angular spread error, and 20.64~ns delay spread error, while accelerating visibility computation by over 25$\times$.

cs.IT↗