Hallucinations in Large Language Models (LLMs), defined as the generation of content inconsistent with facts or context, represent a core obstacle to their reliable deployment in critical domains. Current research primarily focuses on binary "detection" approaches that, while capable of identifying hallucinations, fail to provide interpretable and actionable feedback for model improvement, thus limiting practical utility. To address this limitation, a new research paradigm is proposed, shifting from "detection" to "diagnosis". The Hallucination Diagnosis Task is introduced, a task which requires models to not only detect hallucinations, but also perform error localization, causal explanation, and content correction. We develop the Hallucination Diagnosis Generator (HDG), an automated pipeline that systematically generates high-quality training samples with rich diagnostic metadata from raw corpora through multi-dimensional augmentation strategies including controlled fact fabrication and reasoning chain perturbation. Using HDG-generated data, we train HDM-4B-RL, a 4-billion-parameter hallucination diagnosis model, employing Group Relative Policy Optimization (GRPO) with a comprehensive reward function incorporating structural, accuracy, and localization signals. Experimental results demonstrate that our model surpasses previous state-of-the-art detection models on the HaluEval benchmark while achieving comparable performance to advanced general-purpose models. In comprehensive diagnosis tasks, HDM-4B-RL matches the capabilities of larger general models while maintaining a smaller size. This work validates the feasibility and value of hallucination diagnosis, providing an effective methodology for building more trustworthy and reliable generative AI systems.

LLM-based multi-agent systems have demonstrated significant capabilities across diverse domains. However, the task performance and efficiency are fundamentally constrained by their collaboration strategies. Prevailing approaches rely on static topologies and centralized global planning, a paradigm that limits their scalability and adaptability in open, decentralized networks. Effective collaboration planning in distributed systems using only local information thus remains a formidable challenge. To address this, we propose BiRouter, a novel dual-criteria routing method for Self-Organizing Multi-Agent Systems (SO-MAS). This method enables each agent to autonomously execute "next-hop" task routing at runtime, relying solely on local information. Its core decision-making mechanism is predicated on balancing two metrics: (1) the ImpScore, which evaluates a candidate agent's long-term importance to the overall goal, and (2) the GapScore, which assesses its contextual continuity for the current task state. Furthermore, we introduce a dynamically updated reputation mechanism to bolster system robustness in untrustworthy environments and have developed a large-scale, cross-domain dataset, comprising thousands of annotated task-routing paths, to enhance the model's generalization. Extensive experiments demonstrate that BiRouter achieves superior performance and token efficiency over existing baselines, while maintaining strong robustness and effectiveness in information-limited, decentralized, and untrustworthy settings.

We revisit the question of whether worst-case hardness of the time-bounded Kolmogorov complexity problem, MINK^{poly} - that is, determining whether a string is "structured" (i.e., K^t(x) < n-1) or "random" (i.e., K^{poly(t)} ≥ n-1) - suffices to imply the existence of one-way functions (OWF). Liu-Pass (CRYPTO'25) recently showed that worst-case hardness of a boundary version of MINK^{poly} - where, roughly speaking, the goal is to decide whether given an instance x, (a) x is K^poly-random (i.e., K^{poly(t)}(x) ≥ n-1), or just close to K^poly-random (i.e., K^{t}(x) < n-1 but K^{poly(t)} > n - log n) - characterizes OWF, but with either of the following caveats (1) considering a non-standard notion of probabilistic K^t, as opposed to the standard notion of K^t, or (2) assuming somewhat strong, and non-standard, derandomization assumptions. In this paper, we present an alternative method for establishing their result which enables significantly weakening the caveats. First, we show that boundary hardness of the more standard randomized K^t problem suffices (where randomized K^t(x) is defined just like K^t(x) except that the program generating the string x may be randomized). As a consequence of this result, we can provide a characterization also in terms of just "plain" K^t under the most standard derandomization assumption (used to derandomize just BPP into P) - namely E ̸ ⊆ ioSIZE[2^{o(n)}]. Our proof relies on language compression schemes of Goldberg-Sipser (STOC'85); using the same technique, we also present the the first worst-case to average-case reduction for the exact MINK^{poly} problem (under the same standard derandomization assumption), improving upon Hirahara’s celebrated results (STOC'18, STOC'21) that only applied to a gap version of the MINK^{poly} problem, referred to as GapMINK^{poly}, where the goal is to decide whether K^t(x) ≤ n-O(log n)) or K^{poly(t)}(x) ≥ n-1 and under the same derandomization assumption.

Urban greening trees play a significant role in optimizing the environment, and the segmentation of branches can effectively help researchers assess the growth status of trees. This paper proposed an innovative RGB image-based model to segment tree branches within urban landscapes. The proposed model employed a dual-encoder architecture, including a basic encoder and an edge encoder. These components were designed with specialized blocks adept at extracting critical semantic features and edge information, enhancing the model's ability to segment fine branches. A graph reasoning decoder block with attention-based feature fusion was proposed to capture the semantic associations between regions incorporating edge information. Moreover, the elastic interaction-based loss function, a groundbreaking loss function, was introduced to ensure that the segmentation of the fine branches should be achieved smoothly and consistently. Upon evaluation against a public urban street tree dataset, the precision, recall, IoU, and accuracy of tree branch segmentation are 94.39%, 93.16%, 88.27%, and 98.78%, respectively, achieving the best result among all the tested models. This performance demonstrates the impact of deep learning on enhancing urban greening and sustainable development in effective tree management.

Due to the practical significance in smart video surveillance systems, Text-Based Person Search (TBPS) has been one of the research hotspots recently, which refers to searching for the interested pedestrian images given natural language sentences. To help researchers quickly grasp the developments of this important task, we comprehensively summarize the recent research advances of TBPS from two perspectives,i.e., Feature Extraction (FE) and Semantic Alignments (SA). Specifically, the FE mainly consists of pre-processing approaches and end-to-end frameworks, and the SA could be briefly divided into cross-modal attention mechanism, non-attention alignments, training objectives, and generative approaches. Afterwards, we elaborate four widely-used benchmarks and also the evaluation criterion for TBPS. And comparisons and analyses among the state-of-the-art (SOTA) solutions are provided based on these large-scale benchmarks. At last, we point out some future research directions that need to be further addressed, which will greatly facilitate the practical applications of TBPS.

Only a handful candidates for computational assumptions that imply secure key-agreement protocols (KA) are known, and even fewer are believed to be quantum safe. In this paper, we present a new hardness assumption-the worst-case hardness of a promise problem related to an interactive version of Kolmogorov Complexity. Roughly speaking, the promise problem requires telling apart tuples of strings $(\pi, x, y)$ with relatively (w.r.t. $\mathrm{K}(\pi)$) low time-bounded Interactive Kolmogorov Complexity $\left(\mathrm{IK}^{t}\right)$, and those with relatively high Kolmogorov complexity, given the promise that $\mathrm{K}^{t}(x \mid y)\lt s, \mathrm{~K}^{t}(y \mid x)\lt s$ and $s=\log n$, and where $\mathrm{IK}^{t}(\pi ; x ; y)$ is defined as the length of the shortest pair of t-bounded TMs $(A, B)$ such that the interaction of $(A, B)$ lead to the transcript $\pi$ and the respective outputs $x, y$. We demonstrate that when t is some polynomial, then not only does this hardness assumption imply the existence of KA, but it is also necessary for the existence of secure KA. As such, it yields the first natural hardness assumption characterizing the existence of key-agreement protocols. We additionally show that when the threshold s is bigger (e.g., $s=55 \log n$), then the (worst-case) hardness of this problem instead characterizes the existence of one-way functions (OWFs). As such, our work also clarifies exactly what it would take to base KA on the existence of OWFs, and demonstrates that this question boils down to demonstrating a worst-case reduction between two closely related promise problems.

In order to improve the working efficiency and reliability of energy-harvesting low-power wide area networks (EH-LPWANs) applied in smart forest monitoring, a cross-layer collaboration control model is proposed. Taking the influencing factors of EH-LPWAN as concept nodes, a fuzzy cognitive map (FCM) was devised, and the relationship between each concept was utilized to establish the cross-layer model for optimally satisfying multiple objectives and conflicting constraints. Here, a dynamic FCM scheme based on adaptive glowworm swarm optimization (AGSO) is given to determine the concept weights and sustain online updates. The results show that the energy neutrality of EH nodes and the data throughput of the entire network completely meet the real-time and stability requirements for precise forest monitoring.

We present the first natural NP-complete problem whose average-case hardness w.r.t. the uniform distribution over instances is equivalent to the existence of one-way functions (OWFs). The problem, which originated in the 1960s, is the Conditional Time-Bounded Kolmogorov Complexity Problem: let K^t(x∣z) be the length of the shortest "program" that, given the "auxiliary input" z, outputs the string x within time t(|x|), and let McK^tP[ζ] be the set of strings (x,z,k) where |z| = ζ(|x|), |k| = log |x| and K^t(x∣z) < k, where, for our purposes, a "program" is defined as a RAM machine. Our main result shows that for every polynomial t(n) ≥ n², there exists some polynomial ζ such that McK^tP[ζ] is NP-complete. We additionally extend the result of Liu-Pass (FOCS'20) to show that for every polynomial t(n) ≥ 1.1n, and every polynomial ζ(⋅), mild average-case hardness of McK^tP[ζ] is equivalent to the existence of OWFs. Taken together, these results provide the following crisp characterization of what is required to base OWFs on NP ⊈ BPP: There exists concrete polynomials t,ζ such that "Basing OWFs on NP ⊈ BPP" is equivalent to providing a "worst-case to (mild) average-case reduction for McK^tP[ζ]". In other words, the "holy-grail" of Cryptography (i.e., basing OWFs on NP ⊈ BPP) is equivalent to a basic question in algorithmic information theory. As an independent contribution, we show that our NP-completeness result can be used to shed new light on the feasibility of the polynomial-time bounded symmetry of information assertion (Kolmogorov'68).

We present an open-source toolbox, named MMRotate, which provides a coherent algorithm framework of training, inferring, and evaluation for the popular rotated object detection algorithm based on deep learning. MMRotate implements 18 state-of-the-art algorithms and supports the three most frequently used angle definition methods. To facilitate future research and industrial applications of rotated object detection-related problems, we also provide a large number of trained models and detailed benchmarks to give insights into the performance of rotated object detection. MMRotate is publicly released at https://github.com/open-mmlab/mmrotate.

Let MKtP[s] be the set of strings x such that Kt(x) ≤ s(|x|), where Kt(x) denotes the t-bounded Kolmogorov complexity of the truthtable described by x. Our main theorem shows that for an appropriate notion of mild average-case hardness, for every ε>0, polynomial t(n) ≥ (1+ε)n, and every “nice” class F of super-polynomial functions, the following are equivalent: (i) the existence of some function T ∈ F such that T-hard one-way functions (OWF) exists (with non-uniform security); (ii) the existence of some function T ∈ F such that MKtP[T−1] is mildly average-case hard with respect to sublinear-time non-uniform algorithms (with running-time nδ for some 0<δ<1). For instance, existence of subexponentially-hard (resp. quasi-poly-nomially-hard) OWFs is equivalent to mild average-case hardness of MKtP[poly logn] (resp. MKtP[2O(√logn))]) w.r.t. sublinear-time non-uniform algorithms. We additionally note that if we want to deduce T-hard OWFs where security holds w.r.t. uniform T-time probabilistic attackers (i.e., uniformly-secure OWFs), it suffices to assume sublinear time hardness of MKtP w.r.t. uniform probabilistic sublinear-time attackers. We complement this result by proving lower bounds that come surprisingly close to what is required to unconditionally deduce the existence of (uniformly-secure) OWFs: MKtP[polylogn] is worst-case hard w.r.t. uniform probabilistic sublinear-time algorithms, and MKtP[n−logn] is mildly average-case hard for all O(t(n)/n3)-time deterministic algorithms.

We prove that the equivalence of two fundamental problems in the theory of computing. For every polynomial , the following are equivalent: · One-way functions exists (which in turn is equivalent to the existence of secure private-key encryption schemes, digital signatures, pseudorandom generators, pseudorandom functions, commitment schemes, and more); · t-time bounded Kolmogorov Complexity, Kt, is mildly hard-on-average (i.e., there exists a polynomial such that no PPT algorithm can compute Kt, for more than a 1-[1/p(n)] fraction of n-bit strings). In doing so, we present the first natural, and well-studied, computational problem characterizing the feasibility of the central private-key primitives and protocols in Cryptography.

We have described a new method to construct the reconfigurable XOR logic circuit by using the modification of the standard uncoupled cellular neural network (CNN) cells. The modification of the cell is easier to implement in engineering applications. The scheme proposed in this paper, using the modification of standard uncoupled CNN cells, allows less hardware consumption in comparison to the utilisation of chaos computing system or harnessing piecewise-linear systems. The template parameters of the modified cell have been discussed, and the physical circuit implementing the reconfigurable two-input and three-input XOR function has also been presented.