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10 results for “semantics”
Fast Weight Attention for Continual Learning
arXiv:2608.27763v1 Announce Type: new Abstract: Recurrent fast-weight memories and selective state-space models compress an expanding context into a fixed-size recurrent state, making the state transition an online learning rule. We study this rule under read-after-write autoregressive semantics. For the prefix-prediction objective considered here, the local fast-memory example revealed at step $t$ is the prefix-aligned pair $(\mathbf{x}_t,\mathbf{y}_t)=(\phi(\mathbf{k}_{t-1}),\mathbf{v}_t)$. Th
Aug 31, 2026
A Primer on Computational Semantics for Artificial Intelligence Systems
A new arXiv preprint introduces a pedagogical primer on computational semantics for AI systems, framing linguistic meaning through formal, grounded, and distributional theories while contrasting transformer-based models with human language learning.
Aug 27, 2026
Factorized Hypothesis Search for Evidence-to-Taxonomy Retrieval
A new research paper introduces Factorized Hypothesis Search (FHS), a method to improve retrieval accuracy for large taxonomies when inputs are indirect evidence (e.g., table cells) rather than explicit concepts — addressing what the authors term the 'retrieval readiness gap'.
Aug 10, 2026
EntropyMoE: Entropy-Aware Sparse Expert Routing for Tokenizer-Free LLMs
EntropyMoE is a new Mixture-of-Experts architecture for byte-level, tokenizer-free LLMs that routes computation per dynamic byte patch using entropy as a routing signal, improving compression efficiency without sacrificing downstream task accuracy.
Aug 10, 2026
Towards Multi-Label Graph Foundation Models: from Single-Vector Representation Learning to Multi-Semantic Basis Learning
Researchers propose MSB-GFM, a new graph foundation model architecture designed to handle multi-label node classification across domains by replacing single-vector representations with adaptive multi-semantic basis composition.
Aug 10, 2026
ISEE: Interactive Semantic Enrichment for Database Fields
ISEE is a new interactive system that improves LLM agent performance on data tasks by collaboratively enriching ambiguous database field descriptions with user-provided domain knowledge.
Aug 5, 2026
Semi-Supervised Text-Attributed Graph Distillation
A new semi-supervised graph distillation method called \algo{} is proposed to improve scalability and interpretability of text-attributed graphs (TAGs) when used with large language models, addressing bottlenecks in representation learning.
Jul 24, 2026
Position: Quantum Program Generation Must Prioritize Validity Over Probabilistic Scaling
A position paper on arXiv argues that scaling large language models to quantum circuit synthesis is fundamentally flawed due to quantum computing’s strict mathematical constraints, and proposes verifier-centric, rule-embedded generation instead of probabilistic imitation.
Jul 20, 2026
StateFuse: Deterministic Conflict-Preserving Memory for Multi-Agent Systems
StateFuse is a new conflict-aware memory layer for multi-agent systems that preserves contradictions rather than collapsing them, enabling safer abstention and auditable correction in agent decision loops.
Jul 9, 2026
Gartner Says Lack of Semantics Causes Inaccurate AI Agents and Wasted Spending - Gartner
Gartner identifies insufficient semantic understanding as a root cause of AI agent inaccuracies and inefficient enterprise spending on AI deployments.
Published May 11, 2026 · Analyzed Jul 5, 2026