Large language models (LLMs) are widely applied in various natural language processing tasks such as question answering and machine translation. However, due to the lack of labeled data and the difficulty of manual annotation for biochemical properties, the performance for molecule generation tasks is still limited, especially for tasks involving multi-properties constraints. In this work, we present a two-step framework PEIT (Property Enhanced Instruction Tuning) to improve LLMs for molecular-related tasks. In the first step, we use textual descriptions, SMILES, and biochemical properties as multimodal inputs to pre-train a model called PEIT-GEN, by aligning multi-modal representations to synthesize instruction data. In the second step, we fine-tune existing open-source LLMs with the synthesized data, the resulting PEIT-LLM can handle molecule captioning, text-based molecule generation, molecular property prediction, and our newly proposed multi-constraint molecule generation tasks. Experimental results show that our pre-trained PEIT-GEN outperforms MolT5, BioT5, MolCA and Text+Chem-T5 in molecule captioning, demonstrating modalities align well between textual descriptions, structures, and biochemical properties. Furthermore, PEIT-LLM shows promising improvements in multi-task molecule generation, demonstrating the effectiveness of the PEIT framework for various molecular tasks. The code and appendix are available at https://github.com/chenlong164/PEIT.
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Abstractive summarization plays a crucial role in enabling efficient understanding of scientific literature, yet it inherently demands both linguistic fluency and factual faithfulness. Existing approaches often fail to reconcile these two requirements. Extractive methods rely on rigid sentence splicing that disrupts macro-level logical coherence, while large language model (LLM)-based generative approaches, despite mastering linguistic fluency, exhibit limited factual consistency. In this work, we propose ScholarSum, a hierarchical reflective graph-based framework that emulates a student–teacher writing process for fluent and faithful scientific summarization. ScholarSum first organizes the document into a hierarchical knowledge graph by segmenting it into semantically coherent units, whose multi-layered community structure captures global logic and macro-level themes. Guided by this global structure, the student generates an initial draft, which is subsequently refined through fine-grained evidence retrieval. To ensure factual consistency, a teacher-like reviewer then iteratively examines the draft, identifies unsupported content, and prompts targeted re-retrieval and rewriting until the summary meets rigorous quality standards. Extensive experiments demonstrate that ScholarSum significantly outperforms previous baselines in terms of both completeness and faithfulness. Our code is available at https://github.com/Xiaoyu-Tao/ScholarSum.
Time series forecasting plays a vital role in supporting decision-making across a wide range of critical applications, including energy, healthcare, and finance. Despite recent advances, forecasting accuracy remains limited due to the challenge of integrating historical numerical sequences with contextual features, which often comprise unstructured textual data. To address this challenge, we propose TokenCast, a large language model (LLM) driven framework that leverages language-based symbolic representations as a unified intermediary for context-aware time series forecasting. Specifically, TokenCast employs a discrete tokenizer to transform continuous numerical sequences into temporal tokens, enabling structural alignment with language-based inputs. To effectively bridge the semantic gap between modalities, both temporal and contextual tokens are embedded into a shared representation space via a pre-trained LLM, further optimized with generative objectives. Building upon this unified semantic space, the aligned LLM is subsequently fine-tuned in a supervised manner to predict future temporal tokens, which are then decoded back into the original numerical space. Extensive experiments on real-world datasets demonstrate the effectiveness of our framework and highlight its potential as a generative framework for context-aware time series forecasting. The code is available at https://github.com/Xiaoyu-Tao/TokenCast.
As a fundamental component of the physical layer, convolutional codes are instrumental in ensuring dependable transmission of information over inherently noisy communication channels. Recently, neural network-based decoding algorithms have shown remarkable progress in enhancing the performance of block error-correcting codes. However, current neural network decoders for convolutional codes often exhibit strong performance primarily on low-rate codewords, whereas maximum likelihood decoding algorithm incur substantial time overhead when decoding high-rate codewords, which impacts communication efficiency in practical communication systems. In this work, we propose a Transformer-based decoder for convolutional codes designed for soft-decision decoding over AWGN and fading channels, which serves as an efficient decoding scheme for convolutional codes with various code rates. For the unique structure of convolutional codes, we propose a Bidirectional Context Aggregation mechanism to enable the model to fully capture the long-range dependencies and the memory effect of convolutional structures. Additionally, we extend the Transformer attention matrix with a codeword-specific feature integration mechanism to allow the model to capture implicit relationships within codewords. Extensive experiments demonstrate that the accuracy performance of the proposed model outperforms that of neural network decoders. And for maximum likelihood decoding algorithm, our model achieves comparable error-correction capability and a significantly faster decoding speed.
Building upon powerful Large Visual Language Models, recent GUI agents have revolutionized autonomous GUI interaction. Given the high information density and structural complexity of GUI layouts, a critical challenge lies in accurately identifying where to focus, i.e., precise GUI grounding. To ensure grounding accuracy, prior approaches predominantly rely on processing inputs at native high resolutions. However, this paradigm inevitably introduces excessive visual redundancy, resulting in increased computational cost and dispersed attention. In our preliminary analysis, we observe that while high-resolution inputs are indispensable for recognizing fine-grained visual details, they paradoxically impair the model’s ability to attend to the correct regions. In contrast, low-resolution inputs, which suppress high-frequency details while preserving global layout structure through downsampling, produce significantly more robust and coherent attention patterns that better guide the model toward relevant regions. Interestingly, this observation mirrors the human visual system, which first leverages low-resolution peripheral vision to identify salient areas before conducting a detailed examination via high-resolution foveal focus. Motivated by this insight, we propose a coarse-to-fine GUI grounding framework termed FastFocus. Our approach first exploits the stability of low-resolution attention to efficiently generate candidate grounding regions. These candidates are then evaluated by a Parallel Verification module, which selectively zooms into high-resolution views to resolve fine details and filter out false positives. Experiments on ScreenSpot-Pro demonstrate state-of-the-art performance, validating that a hierarchical integration of low-resolution guidance and high-resolution verification constitutes an effective and robust paradigm for GUI grounding.
Irregular Multivariate Time Series (IMTS) analysis is a challenging task as asynchronous irregular sampling disrupts intra-variable temporal consistency and cross-variable alignment. Most existing methods model multivariate correlations at either the variable or observation level in static ways. They suffer from correlation loss or cross-variable misalignment inevitably. In this paper, we propose a novel method DyH2-STGraph, Dynamic Hierarchical Heterogeneous Spatio-Temporal Graph, for IMTS forecasting. Within the graph, irregular observations are represented as nodes with spatio-temporal coordinates and observed values, connected with other neighbor observation nodes dynamically by spatio-temporal neighbor selection, while variables are treated as hyper nodes, connected with their constituent observation nodes as well as other variable nodes. The multivariate correlations are classified into the following three categories in specific manners: (1) Intra-variable coarse-to-fine correlations between variable nodes and their constituent observation nodes captured by hierarchical message propagation, (2) Inter-variable fine-grained correlations among neighboring observation nodes captured by spatio-temporal attention, and (3) Inter-variable coarse-grained correlations among variable nodes captured by attention. Extensive experiments on four benchmark datasets demonstrate that DyH2-STGraph significantly outperforms state-of-the-art methods while maintaining competitive efficiency.
Retrieval‑augmented generation (RAG) improves knowledge‑intensive QA tasks by incorporating external evidence, yet retrieval remains imperfect and often returns irrelevant or misleading passages. We refer to this as Noisy RAG, where LLMs suffer from inconsistent relevance signals due to the fact that relevance identification is carried out by only a small subset of internal modules (e.g., retrieval heads) and can easily be confounded by other modules. Recent work attempts to enhance the denoising ability of the LLM through external emphasis, prompting the LLM to highlight helpful passages as the emphasis before answering. However, these textual identifications themselves depend on the same inconsistent signals and thus become less reliable. Our key insight is that, because relevance signals reside in only a few modules, emphasis should be applied directly within these modules rather than only at the text level. We therefore propose \textit{selective internal emphasis} to amplify the relevance signals, implemented via a lightweight, plug‑and‑play signal amplifier that operates inside the LLM. The amplifier performs token‑ and channel‑level selective emphasis during a standard RAG fine‑tuning pipeline, with the base LLM frozen. Across four QA benchmarks and three LLM scales, our method consistently improves accuracy and robustness. Furthermore, generalization and interpretability analyses show that the amplifier captures retrieval‑related patterns rather than only dataset‑specific patterns, enabling more reliable passage-denoising.
We study the problem of optimally placing charging stations for a set of k anonymous mobile agents, each of which must reach a distinct terminal. The agents are identical and can travel only up to a given distance r on a single charge. The objective is to find a placement of charging stations so that every terminal can be assigned a unique agent capable of reaching it, and the number of charging stations is minimized. The problem is known to be NP-hard in general and solvable in polynomial time only on paths and cycles. In this work, we comprehensively investigate the classical and parameterized complexity of the problem. We first design a polynomial-time algorithm that, given a set of candidate charging stations, decides whether a feasible assignment of agents to terminals exists. This result also establishes the membership of the problem in NP, which was not previously known. We then analyze the parameterized complexity of the problem. Our results show that the problem remains hard even when we consider different combinations of natural parameters and structural parameters of the given network, such as the distance r plus the tree-depth, or the number of charging stations plus feedback vertex set number. On the positive side, we present three fixed-parameter tractable (FPT) algorithms: one parameterized by the number of agents k, a second by modular-width, and a third by vertex cover number. Finally, we give a polynomial-time k-approximation algorithm, as well as a polynomial-time algorithm for trees, by solving a generalized version of the problem that allows for some charging stations to be pre-placed.
We study EFX and approximate EFX allocations of indivisible goods among agents with at most k distinct valuations (i.e., k types of agents). For exact EFX, it is known that an EFX allocation always exists when there are at most three types of agents. For approximate EFX, the best known guarantee is a 0.618-EFX allocation, and this has been improved recently to a φ-EFX allocation when the number of agents is at most seven. We settle another natural case in this landscape by showing that a φ-EFX allocation exists for any number of agents whenever there are at most four distinct valuations. We then consider a relaxation, EFX with charity, where some goods may remain unallocated and no agent envies the set of unallocated goods. It is known that, for n agents and any ε ∈ (0, 1/2], there exists an EFXε allocation with at most Õ((n/ε)^(1/2)) goods allocated to charity. We prove that when there are at most k distinct valuation types, there exists an EFXε allocation with only Õ((k/ε)^(1/2)) goods allocated to charity; in particular, the required amount of charity is sublinear in the number of distinct valuations rather than in the number of agents. We show that any EFX guarantee for k types of agents can be extended, with an additional (1 − 2ε) approximation factor, to a more general setting where the valuations can be partitioned into k clusters such that the maximum heterogeneity of valuations within each cluster is at most ε.
Multi-robot graph exploration is a central problem in robotics, planning, and multi-agent systems. In this work, we consider the problem of exploring an unknown n-node graph by k robots that must remain connected throughout the process. Such a connectivity is frequently required for safety reasons, and naturally arises in real-world applications such as search-and-rescue and maintenance operations. We study the overhead imposed by not knowing the graph in advance, measured in terms of the competitive ratio of the number of exploration rounds necessary when the graph is unknown (versus the case that it is known). We introduce a novel exploration procedure, DFS-BGS, to tackle the problem, and analyze its performance both theoretically and experimentally. On the theoretical end, DFS-BGS provably achieves a competitive ratio O(k^(1/3)), for the case n < k. Empirically, we compare our online DFS-BGS to COCTA, the SOTA algorithm for trees that are known in advance. Examining the performance of the algorithms on real-world hotel floor plans as well as random graphs over a wide range of parameters, DFS-BGS incurs only a small slowdown, even with hundreds of robots and thousands of nodes.
Effective multimodal learning in real-world scenarios depends on a nuanced treatment of uncertainty, which arises at three levels: (1) Intrinsic Uncertainty from modality-specific noise or ambiguity; (2) Relational Uncertainty due to cross-modal conflicts or redundancy; and (3) Aggregated Uncertainty when fusing potentially inconsistent signals. Most existing methods overlook this hierarchy, applying a single uncertainty model. We propose Adaptive Evidential Multimodal Representation Learning (AEMRL), a framework aligned with this multi-level view. To address intrinsic uncertainty, Disentangled Evidential Uncertainty Encoding (D-EUE) provides interpretable, class-aware reliability scores per modality. For relational uncertainty, Uncertainty-Conditioned Dynamic Factorization (UDF) uses a hypernetwork to dynamically extract complementary cues and suppress conflict. To resolve aggregated uncertainty, Adaptive Fusion & Conflict-Aware Calibration (AFCAC) adaptively weights evidence streams and calibrates final predictions based on detected conflicts. Extensive experiments on five diverse benchmark datasets show that AEMRL consistently enhances task accuracy, reduces calibration error, and improves robustness to noise, semantic conflict, and missing modalities.
Identifying meaningful latent representations within diffusion models remains a challenging problem for causal approaches. We propose Causal Manifold Transport Diffusion Model (CMT-Diff), a framework that operationalizes causal actions as geometric transformations. By adopting the perspective of backtracking counterfactuals, we formulate the generative process as a composite diffeomorphism that couples the Probability Flow ODE with a Continuous Normalizing Flow. This mapping constructs an exogenous manifold where causal factors align with coordinate variations. Within this geometry, we derive Causal Manifold Transport (CMT) to realize interventions as linear vector translations along factor-aligned directions. We establish theoretical identifiability guarantees and demonstrate that our approach facilitates controllable generation by capturing the underlying causal manifold.
Dynamic 3D reconstruction of surgical scenes plays a critical role in robotic-assisted surgery. Gaussian Splatting (GS), while effective for novel view synthesis, struggles to recover accurate surface from a monocular view due to the implicit multi-Gaussian representation of the surface. Specifically, (1) the vertical overlap of Gaussians leads to floating artifacts, and (2) the random orientation of Gaussians affects the smoothness of the reconstructed surface. Consequently, these fragmented and misaligned Gaussians hinder downstream applications, e.g., geometry-aware endoscopic navigation and physics-integrated tissue mechanics simulation. To this end, we propose Endo-GSG, a unified framework that couples dynamic Gaussian splatting with an SDF field for dynamic surface-aware tissue reconstruction. To further enhance geometric fidelity, we design geometry-informed regularization losses that constrain Gaussian density and spatial positioning. The entire pipeline is jointly supervised by RGB images and predicted depth maps, enabling high-quality reconstruction and rendering even with sparse or monocular input. Experiments on public datasets demonstrate that Endo-GSG outperforms state-of-the-art methods in both rendering quality and geometric surface accuracy.
Large Language Models (LLMs) still exhibit limited capability in following complex instructions. While existing approaches often rely on preference learning to enhance this ability, they typically overlook the relationships between the permissible response spaces of different instructions, which restricts a model to align with subtle and diverse constraint variations. To address this, we propose Cross-Relational Preference Learning (CRPL), a novel framework for constructing preference data that explicitly models inter-instruction relationships through two key techniques: Cross-Relationship Perturbation and Cross-Region Pair Sampling. This enables the generation of more diverse preference data that captures a wide spectrum of constraint variations. Additionally, we introduce an atomic constraint-based verification mechanism to rigorously assess response satisfaction, ensuring high-quality preference pair construction. Extensive experiments across multiple preference learning methods (e.g., DPO, KTO), LLM backbones and four instruction-following benchmarks demonstrate that our approach achieves substantial improvements over prior baselines and exhibits strong generalization.
Entity alignment (EA) is critical for knowledge graph (KG) fusion. Existing EA models lack transferability and are incapable of aligning unseen KGs without retraining. While using graph foundation models (GFMs) offer a solution, we find that directly adapting GFMs to EA remains largely ineffective. This stems from a critical "reasoning horizon gap": unlike link prediction in GFMs, EA necessitates capturing long-range dependencies across sparse and heterogeneous KG structures. To address this challenge, we propose a EA foundation model driven by a parallel encoding strategy. We utilize seed EA pairs as local anchors to guide the information flow, initializing and encoding two parallel streams simultaneously. This facilitates anchor-conditioned message passing and significantly shortens the inference trajectory by leveraging local structural proximity instead of global search. Additionally, we incorporate a merged relation graph to model global dependencies and a learnable interaction module for precise matching. Extensive experiments verify the effectiveness of our framework, highlighting its strong generalizability to unseen KGs.
Bayesian optimization is widely employed for optimizing complex black-box functions but struggles with the curse of dimensionality. Random embedding, as a dimension reduction strategy, simplifies tasks that possess the effective dimension by optimizing within a low-dimensional subspace. However, determining the effective dimension of a task in advance remains a significant challenge, which influences the selection of the subspace dimensionality and the optimization performance. Traditional methods use fixed subspace dimensions provided by experts or rely on trial and error to estimate subspace dimensions with resources consumed. To this end, this paper proposes an automated random embedding for high-dimensional Bayesian optimization with unknown effective dimension, called Dynamic Shared Embedding Bayesian Optimization (DSEBO). DSEBO starts with a low dimension and switches to a higher subspace if the solutions in the current subspace show preliminary convergence. DSEBO dynamically determines the dimension of the next subspace based on the quality of the solutions in different subspaces and shares the queried solutions with the new subspace for a better initialization. Theoretically, we derive a regret bound for DSEBO and demonstrate that DSEBO can better balance approximation and optimization errors. Extensive experiments on functions with dimensionality of varying magnitudes and real-world tasks with unknown effective dimensions reveal that, compared with state-of-the-art methods, alternating optimization across different subspaces results in significant improvements in high-dimensional optimization, both in terms of optimization regret and time.
Reconstructing dynamic and interactive 3D scenes from real-world observations remains a fundamental challenge in computer vision and robotics. While recent advances in 3D Gaussian Splatting have enabled high-fidelity static reconstruction, extending it to interactive environments with articulated robots and manipulable objects remains difficult due to complex contact interactions and abrupt pose changes. To address these challenges, we introduce ManiSplat, a unified framework that reconstructs controllable and decoupled Gaussian digital twins directly from monocular ego-view robotic videos. Our method introduces a Graph-Structured Disentangled Representation that separates the robot, objects, and background into independently optimizable Gaussian subfields organized within a scene graph. To ensure stability, we propose a Task-Oriented Spatio-Temporal Alignment module that leverages the inherent logic of manipulation tasks—alternating between Motion and Skill phases—to construct accurate pseudo-ground-truth trajectories. Finally, a joint photometric-geometric optimization ensures the reconstructed scenes are temporally coherent, physically consistent, and simulation-ready. Extensive experiments demonstrate that our approach reconstructs interaction-driven dynamic scenes with high fidelity and controllability, effectively supporting downstream robotic tasks and policy learning.
With the increasing demand of privacy protection, Machine Unlearning (MU) appears to remove private data from an already trained model without retraining from scratch. Most current works suffer from overly unlearning (low fidelity) or incomplete unlearning (low effectiveness). To identify the issues behind, we conduct causal analysis to obtain a resolvable route, i.e., disentangling shared knowledge into attribute-level semantics to remove it as the confounder. We further perform MU loss analysis to reformulate it as balanced form of constraints, thus guaranteeing high fidelity and effectiveness. Based on theoretical analysis, we propose disentangled knowledge forgetting constrained by the reformulated MU loss, which disentangles knowledge with variational auto-encoder and refines knowledge with counterfactual inference. Extensive experimental results demonstrate that our method achieves state-of-the-art performance.
Large language model (LLM)-based text-to-speech (TTS) systems enable prompt-conditioned emotional control but struggle with fine-grained emotion intensity due to the semantic--acoustic gap between text and speech. To address this challenge, we formulate emotion intensity control in LLM-based TTS as a learning-to-rank problem and propose Emo-LiPO, a listwise preference optimization framework that aligns prompt-conditioned speech generation with relative emotion intensity expressed in text. Emo-LiPO explicitly models global intensity ordering within each emotion under fixed transcripts, enabling more faithful and continuous emotional expression. We further construct ESD-plus, a multi-speaker dataset with explicit emotion intensity variations, to support fine-grained emotion modeling and evaluation. Experiments on ESD-plus demonstrate that Emo-LiPO significantly improves emotion accuracy and intensity controllability over both supervised- and DPO-based LLM TTS baselines, with particularly pronounced gains at high intensity levels.
As a key technique for improving throughput by increasing clock frequency and reducing Cycles per Instruction (CPI), pipeline design increasingly relies on automated methods with the growing scale of modern circuits. However, existing automated pipelining methods often decouple partitioning from CPI optimization, implicitly assuming that partitioning affects only clock frequency. We observe that different partitioning strategies significantly alter the pipeline stage differences between logic gates, thereby impacting the average number of stall cycles and ultimately affecting CPI. Thus, automated pipeline partitioning should jointly reduce CPI and improve clock frequency while ensuring functional correctness, resulting in a multi-objective optimization problem. To address this issue, we propose EvoPartition, an evolutionary partitioning framework for automated pipeline design. Specifically, EvoPartition first inserts virtual nodes to guarantee functional correctness. Then, through iterative evolution for reducing CPI and improving clock frequency, pipeline stage assignments of all nodes are determined. Experimental results on ISCAS85 and EPFL show that EvoPartition achieves a 7.34% CPI optimization by reducing stage differences by 16.23%, and further improves throughput by 7.22% compared to the state-of-the-art.