OpenRaiser/Intern-Atlas huggingface dataset:
FORESTLLM: Large Language Models Make Random Forest Great on Few-shot Tabular Learning
Venue: arXiv cs.LG 2026
Year: 2026

Tabular data high-stakes critical decision-making in domains such as finance, healthcare, and scientific discovery. Yet, learning effectively from tabular data in few-shot settings, where labeled examples are scarce, remains a fundamental challenge. Traditional tree-based methods often falter in these regimes due to their reliance on statistical purity metrics, which become unstable and prone to overfitting with limited supervision. At the same time, direct applications of large language models (LLMs) often overlook its inherent structure, leading to suboptimal performance. To overcome these limitations, we propose FORESTLLM, a novel framework that unifies the structural inductive biases of decision forests with the semantic reasoning capabilities of LLMs. Crucially, FORESTLLM leverages the LLM only during training, treating it as an offline model designer that encodes rich, contextual knowledge into a lightweight, interpretable forest model, eliminating the need for LLM inference at test time. Our method is two-fold. First, we introduce a semantic splitting criterion in which the LLM evaluates candidate partitions based on their coherence over both labeled and unlabeled data, enabling the induction of more robust and generalizable tree structures under few-shot supervision. Second, we propose a one-time in-context inference mechanism for leaf node stabilization, where the LLM distills the decision path and its supporting examples into a concise, deterministic prediction, replacing noisy empirical estimates with semantically informed outputs. Across a diverse suite of few-shot classification and regression benchmarks, FORESTLLM achieves state-of-the-art performance.
ProjecTA: A Semi-Humanoid Robotic Teaching Assistant with In-Situ Projection for Guided Tours
Venue: arXiv cs.HC 2026
Year: 2026

Robotic teaching assistants (TAs) often use body-mounted screens to deliver content. In nomadic, walk-and-talk learning, such as tours in makerspaces, these screens can distract learners from real-world objects, increasing extraneous cognitive load. HCI research lacks empirical comparisons of potential alternatives, such as robots with in-situ projection versus screen-based counterparts; little knowledge has been derived for designing such alternatives. We introduce ProjecTA, a semi-humanoid, gesture-capable TA that guides learners while projecting near-object overlays coordinated with speech and gestures. In a mixed-method study (N=24) in a university makerspace, ProjecTA significantly reduced extraneous load and outperformed its screen-based counterpart in perceived usability, usefulness of visual display, and cross-modal complementarity. Qualitative analyses revealed how ProjecTA's coordinated projections, gestures, and speech anchored explanations in place and time, enhancing understanding in ways a screen could not. We derive key design implications for future robotic TAs leveraging spatial projection to support mobile learning in physical environments.
Cutting Corners on Uncertainty: Zonotope Abstractions for Stream-based Runtime Monitoring
Venue: arXiv cs.PL 2026
Year: 2026

Stream-based monitoring assesses the health of safety-critical systems by transforming input streams of sensor measurements into output streams that determine a verdict. These inputs are often treated as accurate representations of the physical state, although real sensors introduce calibration and measurement errors. Such errors propagate through the monitor's computations and can distort the final verdict. Affine arithmetic with symbolic slack variables can track these errors precisely, but independent measurement noise introduces a fresh slack variable upon each measurement event, causing the monitor's state representation to grow without bound over time. Therefore, any bounded-memory monitoring algorithm must unify slack variables at runtime in a way that generates a sound approximation. This paper introduces zonotopes as an abstract domain for online monitoring of RLola specifications. We demonstrate that zonotopes precisely capture the affine state of the monitor and that their over-approximation produces a sound bounded-memory monitor. We present a comparison of different zonotope over-approximation strategies in the context of runtime monitoring, evaluating their performance and false-positive rates.
InterPUF: Distributed Authentication via Physically Unclonable Functions and Multi-party Computation for Reconfigurable Interposers
Venue: arXiv cs.CR 2026
Year: 2026

Modern system-in-package (SiP) platforms increasingly adopt reconfigurable interposers to enable plug-and-play chiplet integration across heterogeneous multi-vendor ecosystems. However, this flexibility introduces severe trust challenges, as traditional authentication schemes fail to scale or adapt in decentralized, post-fabrication programmable environments. This paper presents InterPUF, a compact and scalable authentication framework that transforms the interposer into a distributed root of trust. InterPUF embeds a route-based differential delay physically unclonable function (PUF) across the reconfigurable interconnect and secures authentication using multi-party computation (MPC), ensuring raw PUF signatures are never exposed. Our hardware evaluation shows only 0.23% area and 0.072% power overhead across diverse chiplets while preserving authentication latency within tens of nanoseconds. Simulation results using pyPUF confirm strong uniqueness, reliability, and modeling resistance under process, voltage, and temperature variations. By combining interposer-resident PUF primitives with cryptographic hashing and collaborative verification, InterPUF enforces a minimal-trust authentication model without relying on a centralized anchor.
Latent Space Inference via Paired Autoencoders
Venue: arXiv cs.LG 2026
Year: 2026

This work describes a novel data-driven latent space inference framework built on paired autoencoders to handle observational inconsistencies when solving inverse problems. Our approach uses two autoencoders, one for the parameter space and one for the observation space, connected by learned mappings between the autoencoders' latent spaces. These mappings enable a surrogate for regularized inversion and optimization in low-dimensional, informative latent spaces. Our flexible framework can work with partial, noisy, or out-of-distribution data, all while maintaining consistency with the underlying physical models. The paired autoencoders enable reconstruction of corrupted data, and then use the reconstructed data for parameter estimation, which produces more accurate reconstructions compared to paired autoencoders alone and end-to-end encoder-decoders of the same architecture, especially in scenarios with data inconsistencies. We demonstrate our approaches on two imaging examples in medical tomography and geophysical seismic-waveform inversion, but the described approaches are broadly applicable to a variety of inverse problems in scientific and engineering applications.
Qihe: A General-Purpose Static Analysis Framework for Verilog
Venue: arXiv cs.PL 2026
Year: 2026

In the past decades, static analysis has thrived in software, facilitating applications in bug detection, security, and program understanding. These advanced analyses are largely underpinned by general-purpose static analysis frameworks, which offer essential infrastructure to streamline their development. Conversely, hardware lacks such a framework, which overshadows the promising opportunities for sophisticated static analysis in hardware, hindering achievements akin to those witnessed in software. We thus introduce Qihe, the first general-purpose static analysis framework for Verilog -- a highly challenging endeavor given the absence of precedents in hardware. Qihe features an analysis-oriented front end, a Verilog-specific IR, and a suite of diverse fundamental analyses that capture essential hardware-specific characteristics -- such as bit-vector arithmetic, register synchronization, and digital component concurrency -- and enable the examination of intricate hardware data and control flows. These fundamental analyses are designed to support a wide array of hardware analysis clients. To validate Qihe's utility, we further developed a set of clients spanning bug detection, security, and program understanding. Our preliminary experimental results are highly promising; for example, Qihe uncovered 9 previously unknown bugs in popular real-world hardware projects (averaging 1.5K+ GitHub stars), all of which were confirmed by developers; moreover, Qihe successfully identified 18 bugs beyond the capabilities of existing static analyses for Verilog bug detection (i.e., linters), and detected 16 vulnerabilities in real-world hardware programs. By open-sourcing Qihe, which comprises over 100K lines of code, we aim to inspire further innovation and applications of sophisticated static analysis for hardware, aspiring to foster a similarly vibrant ecosystem that software analysis enjoys.
Relational Linearity is a Predictor of Hallucinations
Venue: arXiv cs.CL 2026
Year: 2026

Hallucination is a central failure mode in large language models (LLMs). We focus on hallucinations of answers to questions like: "Which instrument did Glenn Gould play?", but we ask these questions for synthetic entities that are unknown to the model. Surprisingly, we find that medium-size models like Gemma-7B-IT frequently hallucinate, i.e., they have difficulty recognizing that the hallucinated fact is not part of their knowledge. We hypothesize that an important factor in causing these hallucinations is the linearity of the relation: linear relations tend to be stored more abstractly, making it difficult for the LLM to assess its knowledge; the facts of nonlinear relations tend to be stored more directly, making knowledge assessment easier. To investigate this hypothesis, we create SyntHal, a dataset of 6000 synthetic entities for six relations. In our experiments with four models, we determine, for each relation, the hallucination rate on SyntHal and also measure its linearity, using $Δ\cos$. We find a strong correlation ($r \in [.78,.82]$) between relational linearity and hallucination rate, providing evidence for our hypothesis that the underlying storage of triples of a relation is a factor in how well a model can self-assess its knowledge. This finding has implications for how to manage hallucination behavior and suggests new research directions for improving the representation of factual knowledge in LLMs.
IMS: Intelligent Hardware Monitoring System for Secure SoCs
Venue: arXiv cs.CR 2026
Year: 2026

In the modern Systems-on-Chip (SoC), the Advanced eXtensible Interface (AXI) protocol exhibits security vulnerabilities, enabling partial or complete denial-of-service (DoS) through protocol-violation attacks. The recent countermeasures lack a dedicated real-time protocol semantic analysis and evade protocol compliance checks. This paper tackles this AXI vulnerability issue and presents an intelligent hardware monitoring system (IMS) for real-time detection of AXI protocol violations. IMS is a hardware module leveraging neural networks to achieve high detection accuracy. For model training, we perform DoS attacks through header-field manipulation and systematic malicious operations, while recording AXI transactions to build a training dataset. We then deploy a quantization-optimized neural network, achieving 98.7% detection accuracy with <=3% latency overhead, and throughput of >2.5 million inferences/s. We subsequently integrate this IMS into a RISC-V SoC as a memory-mapped IP core to monitor its AXI bus. For demonstration and initial assessment for later ASIC integration, we implemented this IMS on an AMD Zynq UltraScale+ MPSoC ZCU104 board, showing an overall small hardware footprint (9.04% look-up-tables (LUTs), 0.23% DSP slices, and 0.70% flip-flops) and negligible impact on the overall design's achievable frequency. This demonstrates the feasibility of lightweight, security monitoring for resource-constrained edge environments.
Learning Semantic-Geometric Task Graph-Representations from Human Demonstrations
Venue: arXiv cs.RO 2026
Year: 2026

Learning structured task representations from human demonstrations is essential for understanding long-horizon manipulation behaviors, particularly in bimanual settings where action ordering, object involvement, and interaction geometry can vary significantly. A key challenge lies in jointly capturing the discrete semantic structure of tasks and the temporal evolution of object-centric geometric relations in a form that supports reasoning over task progression. In this work, we introduce a semantic-geometric task graph-representation that encodes object identities, inter-object relations, and their temporal geometric evolution from human demonstrations. Building on this formulation, we propose a learning framework that combines a Message Passing Neural Network (MPNN) encoder with a Transformer-based decoder, decoupling scene representation learning from action-conditioned reasoning about task progression. The encoder operates solely on temporal scene graphs to learn structured representations, while the decoder conditions on action-context to predict future action sequences, associated objects, and object motions over extended time horizons. Through extensive evaluation on human demonstration datasets, we show that semantic-geometric task graph-representations are particularly beneficial for tasks with high action and object variability, where simpler sequence-based models struggle to capture task progression. Finally, we demonstrate that task graph representations can be transferred to a physical bimanual robot and used for online action selection, highlighting their potential as reusable task abstractions for downstream decision-making in manipulation systems.
Low-Rank Key Value Attention
Venue: arXiv cs.LG 2026
Year: 2026

The key-value (KV) cache is a primary memory bottleneck in Transformers. We propose Low-Rank Key-Value (LRKV) attention, which reduces KV cache memory by exploiting redundancy across attention heads, while being compute efficient. Each layer uses a shared full-rank KV projection augmented with low-rank, head-specific residuals, providing a continuous trade-off between complete sharing and full independence. After pretraining models of size 128M to 6.3B parameters, LRKV consistently achieves the lowest test loss among standard MHA, MQA/GQA, and MLA while using only 45-53\% of MHA's KV cache. LRKV reaches equivalent baseline quality 18-25\% faster (measured in training steps). After supervised midtraining, LRKV achieves the highest downstream task performance across ARC-Easy, ARC-Challenge, MMLU, GSM8K, and HumanEval benchmarks.
Space-Optimal, Computation-Optimal, Topology-Agnostic, Throughput-Scalable Causal Delivery through Hybrid Buffering
Venue: arXiv cs.DC 2026
Year: 2026

Message delivery respecting causal ordering (causal delivery) is one of the most classic and widely useful abstraction for inter-process communication in a distributed system. Most approaches tag messages with causality information and buffer them at the receiver until they can be safely delivered. Except for specific approaches that exploit communication topology, therefore not generally applicable, they incur a metadata overhead which is prohibitive for a large number of processes. Much less used are the approaches that enforce causal order by buffering messages at the sender, until it is safe to release them to the network, as the classic algorithm has too many drawbacks. In this paper, first we discuss the limitations of sender-only buffering approaches and introduce the Sender Permission to Send (SPS) enforcement strategy, showing that SPS + FIFO implies Causal. We analyze a recent sender-buffering algorithm, Cykas, which follows SPS + FIFO, albeit very conservatively, pointing out throughput scalability and liveness issues. Then, we introduce a novel SPS + FIFO based algorithm, which adopts a new hybrid approach: enforcing causality by combining sender-buffering to enforce SPS and receiver-buffering to enforce FIFO. The algorithm overcomes limitations of sender-only buffering, and achieves effectively constant metadata size per message. By a careful choice of data-structures, the algorithm is also computationally-optimal, with amortized effectively constant processing overhead. As far as we know, there is no other topology-agnostic causal delivery algorithm with these properties.
CTest-Metric: A Unified Framework to Assess Clinical Validity of Metrics for CT Report Generation
Venue: arXiv cs.CL 2026
Year: 2026

In the generative AI era, where even critical medical tasks are increasingly automated, radiology report generation (RRG) continues to rely on suboptimal metrics for quality assessment. Developing domain-specific metrics has therefore been an active area of research, yet it remains challenging due to the lack of a unified, well-defined framework to assess their robustness and applicability in clinical contexts. To address this, we present CTest-Metric, a first unified metric assessment framework with three modules determining the clinical feasibility of metrics for CT RRG. The modules test: (i) Writing Style Generalizability (WSG) via LLM-based rephrasing; (ii) Synthetic Error Injection (SEI) at graded severities; and (iii) Metrics-vs-Expert correlation (MvE) using clinician ratings on 175 "disagreement" cases. Eight widely used metrics (BLEU, ROUGE, METEOR, BERTScore-F1, F1-RadGraph, RaTEScore, GREEN Score, CRG) are studied across seven LLMs built on a CT-CLIP encoder. Using our novel framework, we found that lexical NLG metrics are highly sensitive to stylistic variations; GREEN Score aligns best with expert judgments (Spearman~0.70), while CRG shows negative correlation; and BERTScore-F1 is least sensitive to factual error injection. We will release the framework, code, and allowable portion of the anonymized evaluation data (rephrased/error-injected CT reports), to facilitate reproducible benchmarking and future metric development.
Coding Schemes for the Noisy Torn Paper Channel
Venue: arXiv cs.IT 2026
Year: 2026

To make DNA a suitable medium for archival data storage, it is essential to consider the decay process of the strands observed in DNA storage systems. This paper studies the decay process as a probabilistic noisy torn paper channel (TPC), which first corrupts the bits of the transmitted sequence in a probabilistic manner by substitutions, then breaks the sequence into a set of noisy unordered substrings. The present work devises coding schemes for the noisy TPC by embedding markers in the transmitted sequence. We investigate the use of static markers and markers connected to the data in the form of hash functions. These two tools have also been recently exploited to tackle the noiseless TPC. Simulations show that static markers excel at higher substitution probabilities, while data-dependent markers are superior at lower noise levels. Both approaches achieve reconstruction rates exceeding $99\%$ with no false decodings observed, primarily limited by computational resources.
ReScene4D: Temporally Consistent Semantic Instance Segmentation of Evolving Indoor 3D Scenes
Venue: arXiv cs.CV 2026
Year: 2026

Indoor environments evolve as objects move, appear, or disappear. Capturing these dynamics requires maintaining temporally consistent instance identities across intermittently captured 3D scans, even when changes are unobserved. We introduce and formalize the task of temporally sparse 4D indoor semantic instance segmentation (SIS), which jointly segments, identifies, and temporally associates object instances. This setting poses a challenge for existing 3DSIS methods, which require a discrete matching step due to their lack of temporal reasoning, and for 4D LiDAR approaches, which perform poorly due to their reliance on high-frequency temporal measurements that are uncommon in the longer-horizon evolution of indoor environments. We propose ReScene4D, a novel method that adapts 3DSIS architectures for 4DSIS without needing dense observations. It explores strategies to share information across observations, demonstrating that this shared context not only enables consistent instance tracking but also improves standard 3DSIS quality. To evaluate this task, we define a new metric, t-mAP, that extends mAP to reward temporal identity consistency. ReScene4D achieves state-of-the-art performance on the 3RScan dataset, establishing a new benchmark for understanding evolving indoor scenes.
CSyMR: Benchmarking Compositional Music Information Retrieval in Symbolic Music Reasoning
Venue: arXiv cs.LG 2026
Year: 2026

Natural language information needs over symbolic music scores rarely reduce to a single step lookup. Many queries require compositional Music Information Retrieval (MIR) that extracts multiple pieces of evidence from structured notation and aggregates them to answer the question. This setting remains challenging for Large Language Models due to the mismatch between natural language intents and symbolic representations, as well as the difficulty of reliably handling long structured contexts. Existing benchmarks only partially capture these retrieval demands, often emphasizing isolated theoretical knowledge or simplified settings. We introduce CSyMR-Bench, a benchmark for compositional MIR in symbolic music reasoning grounded in authentic user scenarios. It contains 126 multiple choice questions curated from community discussions and professional examinations, where each item requires chaining multiple atomic analyses over a score to derive implicit musical evidence. To support diagnosis, we provide a taxonomy with six query intent categories and six analytical dimension tags. We further propose a tool-augmented retrieval and reasoning framework that integrates a ReAct-style controller with deterministic symbolic analysis operators built with music21. Experiments across prompting baselines and agent variants show that tool-grounded compositional retrieval consistently outperforms Large Language Model-only approaches, yielding 5-7% absolute accuracy gains, with the largest improvements on analysis-heavy categories.
Bit-politeia: An AI Agent Community in Blockchain
Venue: arXiv cs.CY 2026
Year: 2026

Current resource allocation paradigms, particularly in academic evaluation, are constrained by inherent limitations such as the Matthew Effect, reward hacking driven by Goodhart's Law, and the trade-off between efficiency and fairness. To address these challenges, this paper proposes "Bit-politeia", an AI agent community on blockchain designed to construct a fair, efficient, and sustainable resource allocation system. In this virtual community, residents interact via AI agents serving as their exclusive proxies, which are optimized for impartiality and value alignment. The community adopts a "clustered grouping + hierarchical architecture" that integrates democratic centralism to balance decision-making efficiency and trust mechanisms. Agents engage through casual chat and deliberative interactions to evaluate research outputs and distribute a virtual currency as rewards. This incentive mechanism aims to achieve incentive compatibility through consensus-driven evaluation, while blockchain technology ensures immutable records of all transactions and reputation data. By leveraging AI for objective assessment and decentralized verification, Bit-politeia minimizes human bias and mitigates resource centralization issues found in traditional peer review. The proposed framework provides a novel pathway for optimizing scientific innovation through a fair and automated resource configuration process.