arXiv subjects

Zitong Wang

Publications and source records attributed to Zitong Wang.

At least 19 recordsLinked to original sources

arXiv2026

ASCoR: Asymmetric Semantic-Spatial Learning for Remote Sensing Domain Generalization

Source-only domain generalization (DG) for remote sensing semantic segmentation seeks reliable predictions in unseen domains without target-domain data during training. Although vision foundation models offer transferable knowledge, category recognition remains sensitive to source-specific visual cues, while task-specific adaptation may weaken pretrained transferability. We propose ASCoR, an asymmetric semantic-spatial framework built on SAM3. Its text-conditioned semantic branch uses class-name prompts to provide explicit category guidance. Probe-Once Dynamic Adaptive LoRA distributes a fixed adaptation budget across encoder blocks to limit source-specific specialization. A complementary task-specific dense branch learns multi-scale spatial representations using a separate frozen SAM3 image encoder. A lightweight reliability router integrates the two branches through bounded spatial correction based on local prediction reliability. All components are trained using source-domain supervision only. Experiments on the ISPRS, LoveDA, and OpenEarthMap benchmarks cover nine cross-domain transfers. ASCoR achieves a state-of-the-art mean IoU of 68.61% across six ISPRS transfers, outperforming the previous best method by 2.69 percentage points. It also achieves the best results on all three LoveDA/OpenEarthMap transfers, with 59.07%, 47.95%, and 49.38% mIoU on Rural-to-Urban, Urban-to-Rural, and Urban-to-OpenEarthMap, respectively. Ablation studies, feature visualizations, and spatial reliability analyses support the effectiveness and complementarity of the proposed components. Code: https://github.com/WangZitong42/ASCoR.

cs.CV↗

arXiv2026

StateLoop: Persistent State for Looped Transformers

Looped Transformers offer a parameter-efficient approach to language modeling by repeatedly applying a shared backbone. Vanilla looped Transformers use each backbone output directly as the input to the next iteration. These outputs may contain information useful several iterations later, but retaining it across the loop depends entirely on the single hidden state. We introduce StateLoop, which maintains one additional persistent state per token to retain information across iterations. At each iteration, a learned gate controls how much of the previous state is retained and how much information from the backbone output is incorporated. The updated state is projected back and added to the backbone output for subsequent iterations and final prediction. This design reduces Pile perplexity and improves average accuracy across eight zero-shot tasks over Vanilla Looped. Its perplexity advantage over Vanilla Looped grows with trained loop depth, consistent with persistent state becoming increasingly useful as recurrent computation deepens.

cs.CL↗

arXiv2026

LoopFold: Parallelizing Recurrent Depth in Looped Transformers

Looped Transformers increase computation per token by repeatedly applying a shared backbone, offering a way to improve language modeling and downstream task performance at a fixed backbone parameter count. However, each loop depends on the preceding loop, so increasing recurrent depth also increases decoding latency. Existing approaches include Parallel Loop Transformers, which reduce latency by overlapping loop computation across token positions, but yield rapidly diminishing marginal accuracy gains at larger depths. To address this challenge, we introduce LoopFold, which folds computation at multiple recurrent depths into parallel batches for the same token prefix. A lightweight predictor estimates inputs to later loops so they can start before earlier loops finish. A corrector then refines the outputs of these later loops using newly available results from earlier loops. All components are trained jointly using only the final next-token prediction loss. In experiments with 1.4B models, deeper LoopFold configurations achieve progressively higher average zero-shot accuracy across eight tasks, rising from 53.41% to 56.38%. Vanilla looped models and PLT show limited gains over the same depth range. Compared with a nine-loop vanilla model, LoopFold uses five sequential stages and additional parallel computation to reduce decoding latency by 46%.

cs.CL↗

arXiv2026

P2N: Reusing Deep Representations for Greater Effective Depth

Mathematical problem solving and autonomous agents are increasingly important applications of language models, demanding stronger reasoning capabilities. Computational depth is a key resource for such reasoning, but adding model layers increases both training and inference costs, while test-time scaling incurs substantial additional inference computation. We introduce P2N (Previous to Next), which reuses the previous token's deep representations, already available during generation, to increase effective computational depth with negligible decoding overhead. Specifically, P2N adds the previous token's deep hidden state to the current token's shallow representation, allowing subsequent layers to build on previously completed deep computation. This recurrence extends effective computational depth across tokens, while introducing only a single vector addition per decoding step. We train the recurrence using parallel Jacobi updates restricted to a middle-layer core. Experiments with Qwen-style models show that, at 1.5B parameters, P2N improves GSM8K accuracy by over 10 percentage points under both zero-shot and few-shot prompting, with nearly unchanged decoding latency. The benefits also extend to language modeling and average performance on general downstream benchmarks. on general downstream benchmarks.

cs.CL↗

arXiv2026

Dual-Slot: From Serial Loops to Parallel Slots in Language Models

Looped language models increase computational depth by repeatedly applying shared layers, offering a way to improve model quality without adding parameters. However, sequential dependencies between loop iterations increase autoregressive decoding latency. We introduce Dual-Slot, an architecture that reorganizes serial looped computation into parallel latent and prediction slots. Rather than waiting for the current token's latent computation to finish, the prediction slot combines the previous token's deep latent state with the current representation, while the latent slot produces a state for the next decoding step. This shifts the latent dependency across adjacent tokens, replacing two sequential module invocations with a single paired invocation while preserving standard autoregressive generation. To preserve token-level parallelism during training, we use a small number of Jacobi iterations to refine latent states across the sequence in parallel. Experiments on Qwen-style dense and mixture-of-experts (MoE) models show consistent improvements in language modeling and average downstream accuracy. On the 1B dense model, Dual-Slot reduces Pile validation perplexity from 7.25 to 7.00 and improves average five-shot accuracy across nine task configurations by 2.26 percentage points. After supervised fine-tuning, it improves zero-shot GSM8K accuracy by 5.83 and 3.49 percentage points for the 1B dense and A0.6B MoE models, respectively. Across the evaluated batch sizes, Dual-Slot retains 93.1-96.5% of vanilla decoding throughput for the dense model and 98.1-99.6% for the MoE model.

cs.CL↗

arXiv2026

Denoising Implicit Variational Inference

Semi-implicit variational inference (SIVI) expands the representational capacity of variational families, but optimizing the Evidence Lower Bound (ELBO) remains challenging because the marginal score of the semi-implicit distribution is generally intractable. Unbiased Implicit Variational Inference (UIVI) and related methods address this difficulty through reparameterized ELBO gradients, but existing approaches estimate the required score indirectly through reverse-conditional sampling or approximation, which can be computationally costly and unstable. We propose Denoising Implicit Variational Inference (DIVI), which learns the marginal score directly via denoising score matching. The learned score is used as a plug-in estimator in the pathwise ELBO gradient, replacing inner reverse-conditional sampling with a score-network evaluation. We analyze DIVI as inexact stochastic gradient ascent and show, under stated assumptions, that the averaged stationarity measure is controlled by the usual optimization term and the average score-matching error. Empirically, we evaluate DIVI on both synthetic distributions and a variety of real-data Bayesian inference tasks. The results show that DIVI improves over the current UIVI baselines while reducing the cost of ELBO-gradient estimation.

stat.ML↗

arXiv2026

EgoBench: An Interactive Egocentric Multimodal Benchmark for Tool-Using Agents

As AI agents increasingly operate in open, real-world environments, they require a deep synergy of multimodal perception, tool invocation with multi-hop reasoning, and dynamic interaction with users. However, existing benchmarks fail to jointly evaluate these capabilities due to challenges in designing strictly coupled multi-capability tasks, simulating natural and task-constrained user feedback, and ensuring objective evaluation of dynamic interaction. To bridge this gap, we introduce EgoBench, the first interactive multimodal benchmark for tool-using agents. EgoBench comprises 1,590 egocentric-video-grounded tasks covering five daily scenarios, along with a user-agent-tool interactive environment for evaluation. We implement a three-stage synergistic pipeline through which each task is designed to enforce the joint application of visual perception and tool-augmented multi-hop reasoning. We additionally develop a multi-agent simulated user to evaluate agents' interaction capabilities, which generates high-fidelity, task-aligned responses to agents. Furthermore, we establish a deterministic joint validation framework that guarantees objective assessment through process-based and result-based equivalence. Benchmarking eight video-MLLM agents on EgoBench reveals a severe performance ceiling: the best-performing model achieves only a 34.95% average Joint Success Rate across the three interaction modes. Finally, we conduct a multi-dimensional error analysis to disentangle failure modes, exposing capability bottlenecks for advancing future AI agents.

cs.AI↗

arXiv2026

Emotion Understanding in Streaming Video with Trajectory-Aware Reliability

Video emotion understanding is commonly studied as an offline classification problem, where the complete video segment is available before prediction. Real-time interaction, however, requires emotion decisions from incomplete and evolving evidence. This paper studies streaming video emotion understanding as a reliability-aware decision process over evolving emotion beliefs. In this setting, a single confident prefix prediction can still be unreliable when the underlying belief trajectory is unstable or repeatedly switches across emotion classes. We propose TRACE, a trajectory-aware reliability framework that forms low-latency emotion beliefs from streaming audio prefixes, estimates reliability from confidence, entropy, stability, and class-switching patterns, and selectively invokes contextual belief reinterpretation with visual, textual, and neighboring-utterance evidence. TRACE keeps stable cases in the low-latency online pathway while allocating stronger multimodal reasoning to uncertain cases that remain ambiguous. Experiments on StreamMER, MELD, and MER2024 show that TRACE improves the accuracy-cost trade-off, retaining most full-context gains while reducing unnecessary contextual reasoning.

cs.MM↗

arXiv2026

Delta-K: Boosting Multi-Instance Generation via Cross-Attention Augmentation

While Diffusion Models excel in text-to-image synthesis, they frequently suffer from catastrophic concept omission when generating complex multi-instance scenes. Existing training-free methods attempt to resolve this by rescaling attention maps, which merely exacerbates unstructured noise without establishing coherent semantic representations. To address this, we propose Delta-K, a backbone-agnostic, plug-and-play inference framework that resolves omission by operating directly in the shared cross-attention Key space. Utilizing a lightweight Vision-Language Model (VLM) preview, we isolate a differential key ($ΔK$) capturing the pure semantic signature of missing concepts, and proactively inject it during the early semantic planning phase. Governed by a dynamically optimized scheduling mechanism, Delta-K grounds diffuse noise into stable structural anchors while naturally preserving existing concepts via the inherent orthogonality of $ΔK$. Extensive experiments validate its universal applicability, demonstrating that Delta-K significantly improves compositional alignment across both modern DiT and foundational U-Net architectures without requiring spatial masks, auxiliary training, or structural modifications.

cs.CV↗

arXiv2026

ToolSearcher: Optimizing Tool Selection at Scale via Reinforcement Learning

Large language models (LLMs) excel at natural language processing but struggle to interact with external environments. Tool learning provides a promising way to extend LLMs into actionable agents, where tool selection is a critical prerequisite for successful tool use. Existing work often assumes a small or predefined set of tools, leaving large-scale tool selection underexplored. Real-world repositories contain a vast and diverse array of tools, making it difficult for LLMs to effectively search, distinguish, and compose tools under context-length constraints. We identify large-scale tool selection as a new challenge for agentic reinforcement learning, highlighting that existing RL methods for knowledge-based question answering are inadequate for selecting tools while considering compatibility. To address this challenge, we propose ToolSearcher, a novel RL framework for effective multi-turn search and fine-grained optimization in large-scale tool selection. Specifically, we introduce category-constrained tool discrimination to improve the model's ability to distinguish functionally similar tools, event-level search modeling to explicitly optimize the discovery of target tools during multi-turn search, and trajectory-aligned credit allocation to provide fine-grained reward signals for different stages of the search-selection process. Extensive experiments on large-scale tool selection benchmarks demonstrate that ToolSearcher consistently outperforms a set of strong baselines in challenging settings involving iterative search and complex tool composition.

cs.CL↗

arXiv2026

ProofVerifier: A Scalable, Diversity-Driven Framework for Natural-Language Proof Verification

While large language models (LLMs) have achieved strong performance on mathematical problems with verifiable answers, many advanced problems are proof-based and require evaluating full proofs. However, training such verifiers requires diverse and trustworthy question-proof-check (QPC) examples at scale, which are scarce. To address this challenge, we develop a human-audited, LLM-assisted data pipeline that produces large-scale QPC triplets with limited human effort. By systematically varying problem sources, generation strategies, and generator models, the pipeline creates diverse problem-proof pairs spanning multiple difficulty levels, linguistic styles, and error types. We combine multi-LLM agreement with hierarchical human auditing to obtain accurate proof-correctness labels. Using these data, we train generative proof verifiers and introduce an auxiliary fluency filter together with balanced token weighting to stabilize binary-reward long-form verification RL. Experiments show that our verifier improves proof-judgment accuracy across different proof styles and provides useful guidance for test-time selection. Overall, our results provide a practical data and training framework for natural-language proof verification.

cs.CL↗

arXiv2026

SAGE: Semantic-Aware Geographic Error Recovery for AI Data Movement

AI interconnects typically protect and replay packets uniformly, yet numerical bit faults differ sharply in consequence: a low-order mantissa flip may resemble quantization noise, while a high-significance exponent flip can produce a catastrophic outlier or non-finite value. We present SAGE, a semantic-aware geographic error-recovery architecture that decouples whether a detected fault merits replay from where replay restarts. For BF16-like data, a workload-calibrated contract separates catastrophic Class-H faults from bounded Class-M and precision Class-L damage. It first applies a Class-H silent-delivery constraint, then ranks admissible policies by quality-normalized terminal latency, $Ψ_{\rm del}$. Independently, a source-local region table adapts checkpoint intervals to fault geography, shortening recovery segments in noisy regions. Detected Class-H failures may trigger protected negative acknowledgments and full-flit replay; Class-M and Class-L outcomes do not trigger default network replay. We implement SAGE's endpoint and replay protocol in gem5 Garnet and synthesize its fully pipelined checker in ASAP7. At a stable synthetic operating point, a ten-seed contention-faithful direct-Garnet campaign shows that SAGE reduces $Ψ_{\rm del}$ by 30.1% relative to fixed 34-hop recovery, combining 28.0% lower mean latency with improved delivered semantic quality. Under higher-BER synthetic stress at the same offered load, SAGE maintains bounded queues while the fixed baseline accumulates backlog. Application-derived DeiT-S communication traces also show lower mean and p99.5 latency at the evaluated nonzero BERs. Within the qualified operating envelope, CRC32 decoder trials yield a simultaneous 95% per-original Class-H silent-delivery upper bound of $3.18\times10^{-7}$.

cs.AR↗

arXiv2026

UniMoMo: Expert Merging-Based MoE Acceleration for Large Recommendation Models

Sparse mixture-of-experts (MoE) layers expand recommendation capacity through conditional computation, yet a trained checkpoint still stores and routes over its full expert bank. We study a deployment problem: convert that checkpoint to a smaller standard MoE under an explicit expert budget, without adding a compression-specific online module. To address this, we introduce UniMoMo, a post-training compression framework formulated as a constrained graph coarsening problem. Rather than relying on parameter distance, UniMoMo groups experts based on their functional similarity, using an unlabeled calibration set to measure how similarly experts respond to shared recommendation states. To prevent performance degradation, we introduce a layer-adaptive protection mechanism that restricts the merging of high-traffic experts based on their routing exposure. Across Amazon Beauty, KuaiRec, and TenRec with 2, 4, and 6 MoE blocks, the final four-expert checkpoints obtain source-relative five-run mean NDCG@10 ratios of 99.92%--102.30% and measured A100 speedups of 1.28$\times$--1.63$\times$. An aggressive two-expert, top-1 operating point obtains ratios of 98.36%--104.24% and speedups of 1.47$\times$--2.21$\times$. These endpoint results evaluate the complete conversion-and-adaptation workflow and show that a trained recommendation MoE can be exported at multiple serving budgets.

cs.AI↗

arXiv2026

Rank-Aware Element Grouping for Power-Efficient Multiuser ISAC With an Extremely Large-Scale IRS

We investigate power-efficient multiuser integrated sensing and communication (ISAC) assisted by an element-grouping extremely large-scale intelligent reflecting surface (EG-XL-IRS). The grouping pattern is designed using slowly varying statistical channel state information (S-CSI), so that both IRS-related channel acquisition and online passive beamforming operate in the group domain rather than the element domain. We reveal a fundamental gain-rank tradeoff induced by element grouping: phase-consistent grouping can coherently enhance selected deterministic propagation components, while excessive concentration on a common deterministic mode can reduce the effective spatial rank of the multiuser channel and, for extended targets, the diversity of desired-scatterer responses. Motivated by this observation, we develop a task-adaptive rank-aware grouping strategy that balances weak-user enhancement and target-scatterer illumination while preserving task-relevant spatial dimensions. For each candidate grouping pattern, the transmit covariances and group-wise reflection phases are jointly optimized under communication and sensing quality-of-service constraints, followed by physical phase recovery and feasibility verification. Numerical results show that the proposed design substantially reduces the required transmit power compared with representative grouping benchmarks under the same grouping dimension and online optimization budget.

eess.SP↗

arXiv2026

A Multi-Branch Hierarchy-Aware Framework for Heterogeneous Audio Classification

This technical report describes our system for Task 1 of the DCASE 2026 Challenge, which aims to classify heterogeneous audio recordings according to the Broad Sound Taxonomy (BST). The task requires both accurate second-level prediction and consistency with the top-level taxonomy. Our system is built on CLAP-based audio-text representations and is improved along three strategies: expanding the training set with a filtered subset of BSD35k, enhancing acoustic modeling with feature-specific branches, and refining predictions using hierarchy-aware classifiers and KNN-based post-processing. Among the acoustic features considered, the log-STFT branch provides the strongest single-model performance. With KNN-based post-processing, our best single system achieves a hierarchical F1 score (Hier. F1) of 80.84% on the BSD10k-v1.2 set under the same evaluation protocol as the baseline. We further construct ensemble systems by combining models with complementary acoustic features and classification heads, achieving Hier. F1 scores of 81.25% and 81.18%, respectively.

cs.SD↗

arXiv2026

Deep learning with hybrid frequency differencing and principal component analysis for 21-cm foreground and beam mitigation

Twenty-one-centimeter intensity mapping is a powerful probe of the large-scale distribution of neutral hydrogen (HI) and cosmological observables such as baryon acoustic oscillations. A major challenge is contamination from bright foregrounds and frequency-dependent beam effects, which can lead to signal loss in traditional methods such as principal component analysis (PCA). We develop a hybrid approach that trains a U-shaped convolutional neural network (UNet) on two input channels derived from frequency differencing (FD) and PCA cleaning, enabling it to exploit their complementary behavior across different scales. This two-channel strategy achieves improved performance, maintaining the cross-correlation power spectrum close to unity on large scales under a cosine beam and improving by 5\%-8\% relative to either FD- or PCA-based UNet alone. We further show that the method can robustly recover the HI signal even when the beam model is imperfect and differs between training and testing, with the large-scale cross-correlation remaining close to unity within the $1σ$ level. These results demonstrate that the proposed approach provides a robust framework for HI signal reconstruction under realistic observational conditions.

astro-ph.CO↗

arXiv2026

Depth-Attention: Cross-Layer Value Mixing for Language Models

Self-attention selects information freely across the sequence, but across depth, Transformers merely add each layer's output to the residual stream, so later layers cannot selectively reuse earlier-layer representations. Recent cross-layer methods improve this flow but operate on hidden states outside attention, adding state beyond the key-value cache at inference--a cost that becomes increasingly salient as modern LLMs compress the cache with grouped-query and multi-head latent attention. We introduce Depth-Attention, which performs this selection inside the attention module itself: before a layer attends over the sequence, its query attends over the keys of earlier layers at the same token position and mixes their values into the value that self-attention then reads. Because Depth-Attention reuses the standard attention queries, keys, and value-cache slots, storing depth-mixed values in place of the original values, it adds no parameters and introduces no persistent inference state beyond the standard key-value cache--the same cache size as a vanilla decoder and less than hidden-state-based cross-layer methods. On Qwen3-style decoders at 1.5B and 3B parameters, Depth-Attention attains the lowest perplexity and the highest average downstream accuracy, improving over the vanilla Transformer by up to 2.3 accuracy points and surpassing strong cross-layer baselines in perplexity and average accuracy, while adding under 0.01% extra arithmetic FLOPs and no additional persistent inference state. The gains hold from 360M to 3B parameters and extend to looped Transformers.

cs.CL↗

arXiv2026

QPLEX Decision Processes: Formulation via Nonlinear Markov Chains and Optimization via Policy Gradients

We introduce a QPLEX Decision Process (QDP) as a model for dynamic control of queueing systems with non-stationary arrivals, general service distributions, and service-level chance constraints. QDPs integrate QPLEX, a computational modeling methodology for transient analysis of stochastic systems, into a nonlinear Markov decision framework. Since QPLEX approximations use nonlinear transition probabilities with orders-of-magnitude smaller state spaces, QDPs circumvent the curse of dimensionality associated with general service times. Via forward and backward iterative schemes, we can rapidly compute gradients deterministically on the much smaller state space, eliminating sampling variance. We further address optimization through natural-gradient-inspired methods with block-diagonal Fisher approximations. To illustrate the QDP methodology, we formulate a single-station dynamic pricing problem with non-stationary demand as a QDP. When the reward structure uses waiting and terminal costs, our approach can find near-optimal policies in seconds on a single CPU; when the reward structure uses penalties for deviating from service-level chance constraints, the optimization landscape is substantially more challenging yet our approach can find a high-quality, practical policy in approximately a minute on a single CPU.

math.OC↗