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From Internal Diagnosis to External Auditing: A VLM-Driven Paradigm for Data-Free Online Backdoor Defense

arXiv:2601.19448v2 Announce Type: replace Abstract: Deep Neural Networks remain inherently vulnerable to backdoor attacks. Traditional test-time defenses largely operate under the paradigm of internal diagnosis methods like model repairing or input robustness, yet these approaches are often fragile under advanced attacks as they remain entangled with the victim model's corrupted parameters. We propose a paradigm shift from Internal Diagnosis to External Semantic Auditing, arguing that...

arXiv CS 9d ago

Hyperspectral Smoke Segmentation via Mixture of Prototypes

arXiv:2602.10858v2 Announce Type: replace Abstract: Smoke segmentation is critical for wildfire management and industrial safety applications. Traditional visible-light-based methods face limitations due to insufficient spectral information, particularly struggling with cloud interference and semi-transparent smoke regions.

arXiv CS 1d ago

Wi-Fi Router vs. Mesh System: Which Is Best for You?

We all want a good, strong Wi-Fi signal throughout our homes, but figuring out how to achieve it without overspending can be tricky. Even once you’ve decided to replace the router from your internet service provider, do you get a new Wi-Fi router or invest in a mesh system? I covered this briefly in my router buying guide, but I’m going to dive a bit deeper here.

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Dreaming when Necessary: Advancing World Action Models with Adaptive Multi-Modal Reasoning

arXiv:2606.07089v1 Announce Type: new Abstract: World Action Models (WAMs) offer a promising approach to embodied intelligence, yet existing methods rely heavily on video prediction as action priors and lack adaptive multimodal reasoning, limiting their effectiveness on long-horizon, complex tasks. We observe that WAMs require different multimodal reasoning modes under different execution contexts: textual reasoning is essential during task transitions to guide high-level action prediction,...

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MosaicIMU: Composing Carrier Experts for Generalizable Neural Inertial Odometry

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Dynamic Interaction-Aware and Causality-Disentangled Framework for Multimodal Sentiment Analysis

arXiv:2605.30994v1 Announce Type: new Abstract: Although Multimodal Sentiment Analysis (MSA) effectively leverages rich information from language, visual, and acoustic modalities, existing methods still face two core challenges: 1) static conflict suppression mechanisms fail to adapt to dynamic variations across samples, and 2) the inherent sentimental bias within the language modality, which can misguide learning from other modalities, remains entangled. To this end, we propose a Dynamic...

arXiv CS 9d ago

AMP: A Vendor-Neutral Wire Format for Agent Memory Operations

arXiv:2606.01138v1 Announce Type: new Abstract: Agent-memory frameworks - mem0, Letta/MemGPT, Cognee, Zep/Graphiti, MemoryOS, MemTensor - each ship their own SDK, storage layout, and operational vocabulary. There is no shared wire format: every integration is bespoke, every migration rebuilds memory from scratch, and no framework ships a governance surface that lets a human review writes before they enter long-term storage. We present memorywire, a JSON-Schema 2020-12 wire format for five...

arXiv CS 8d ago

memorywire: A Vendor-Neutral Wire Format for Agent Memory Operations

arXiv:2606.01138v2 Announce Type: replace Abstract: Agent-memory frameworks -- mem0, Letta/MemGPT, Cognee, Zep/Graphiti, MemoryOS, MemTensor -- each ship their own SDK, storage layout, and operational vocabulary. There is no shared wire format: every integration is bespoke, every migration rebuilds memory from scratch, and no framework ships a governance surface that lets a human review writes before they enter long-term storage. We present memorywire, a JSON-Schema 2020-12 wire format for...

arXiv CS 6d ago

TARPO: Token-Wise Latent-Explicit Reasoning via Action-Routing Policy Optimization

Announce Type: new Abstract: Latent reasoning has emerged as a promising alternative to discrete Chain-of-Thought (CoT) in large language models (LLMs), enabling more expressive reasoning by operating over continuous representations. However, the inherently deterministic nature of continuous representations limits policy exploration in reinforcement learning (RL). To address this, we propose TARPO (Token-Wise Latent-Explicit Reasoning via Action-Routing Policy Optimization), a pure RL...

arXiv CS 5d ago

LayerRoute: Input-Conditioned Adaptive Layer Skipping via LoRA Fine-Tuning for Agentic Language Models

arXiv:2606.01838v1 Announce Type: new Abstract: Agentic language model systems alternate between two structurally distinct step types: structured tool calls (short, deterministic, low perplexity) and open-ended planning/reasoning steps (long, complex, high perplexity). Despite this heterogeneity, current inference systems apply identical compute to every step. We introduce LayerRoute, a lightweight adapter that learns to selectively skip transformer blocks on a per-input basis.

arXiv CS 8d ago