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cs.CL updates on arXiv.org

Playing Along: Learning a Double-Agent Defender for Belief Steering via Theory of Mind RPA-Check: A Multi-Stage Automated Framework for Evaluating Dynamic LLM-based Role-Playing Agents A Triadic Suffix Tokenization Scheme for Numerical Reasoning Hidden Measurement Error in LLM Pipelines Distorts Annotation, Evaluation, and Benchmarking Synthius-Mem: Brain-Inspired Hallucination-Resistant Persona Memory Achieving 94.4% Memory Accuracy and 99.6% Adversarial Robustness on LoCoMo Time is Not a Label: Continuous Phase Rotation for Temporal Knowledge Graphs and Agentic Memory NovBench: Evaluating Large Language Models on Academic Paper Novelty Assessment Policy Split: Incentivizing Dual-Mode Exploration in LLM Reinforcement with Dual-Mode Entropy Regularization METER: Evaluating Multi-Level Contextual Causal Reasoning in Large Language Models Think Before you Write: QA-Guided Reasoning for Character Descriptions in Books METRO: Towards Strategy Induction from Expert Dialogue Transcripts for Non-collaborative Dialogues Retrieval as Generation: A Unified Framework with Self-Triggered Information Planning Do LLMs Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations Enhancing Multimodal Large Language Models for Ancient Chinese Character Evolution Analysis via Glyph-Driven Fine-Tuning Polyglot Teachers: Evaluating Language Models for Multilingual Synthetic Data Generation Exploring Knowledge Conflicts for Faithful LLM Reasoning: Benchmark and Method CocoaBench: Evaluating Unified Digital Agents in the Wild MathAgent: Adversarial Evolution of Constraint Graphs for Mathematical Reasoning Data Synthesis Evaluating Memory Capability in Continuous Lifelog Scenario How Robust Are Large Language Models for Clinical Numeracy? An Empirical Study on Numerical Reasoning Abilities in Clinical Contexts Efficient Training for Cross-lingual Speech Language Models Shared Emotion Geometry Across Small Language Models: A Cross-Architecture Study of Representation, Behavior, and Methodological Confounds A Systematic Analysis of the Impact of Persona Steering on LLM Capabilities Uncertainty-Aware Web-Conditioned Scientific Fact-Checking When Valid Signals Fail: Regime Boundaries Between LLM Features and RL Trading Policies When Verification Fails: How Compositionally Infeasible Claims Escape Rejection Mem$^2$Evolve: Towards Self-Evolving Agents via Co-Evolutionary Capability Expansion and Experience Distillation AOP-Smart: A RAG-Enhanced Large Language Model Framework for Adverse Outcome Pathway Analysis OccuBench: Evaluating AI Agents on Real-World Professional Tasks via Language Environment Simulation Advancing Polish Language Modeling through Tokenizer Optimization in the Bielik v3 7B and 11B Series
Arch: An AI-Native Hardware Description Language for Regi...
Shuqing Zhao · 2026-04-07 · via cs.CL updates on arXiv.org

We present Arch (AI-native Register-transfer Clocked Hardware), a hardware description language for micro-architecture specification and AI-assisted code generation. Arch provides first-class constructs for pipelines, FSMs, FIFOs, arbiters, register files, buses with handshake channels, clock-domain crossings, and multi-cycle threads -- structures that existing HDLs express only as user-defined patterns prone to subtle errors. A central design choice is that clocks and resets are parameterized types (Clock<D>, Reset<S,P,D?>) rather than ordinary nets, converting CDC and reset-domain analysis from external linter passes into compile-time typing rules. Bit widths, port directions, single-driver ownership, and combinational acyclicity are tracked in the same pass, catching latches, width mismatches, loops, and unsynchronized crossings before simulation. A guard clause on reg declarations captures the valid-data pattern declaratively, catching the producer bug where a valid flag asserts before data is written. Every syntactic choice is governed by an AI-generatability contract: an LL(1) grammar, no preprocessor, a uniform declaration schema, named block endings, and a todo! escape hatch let LLMs produce structurally correct, type-safe Arch from natural-language specs without fine-tuning. The compiler emits lint-clean IEEE 1800-2017 SystemVerilog and auto-generates safety properties (FIFO no-overflow, counter range, FSM legal-state, handshake protocol) verified with Verilator -- assert and EBMC, plus direct AST-to-SMT-LIB2 bounded model checking via arch formal. An integrated simulator compiles designs to native C++ with Python cocotb support. Case studies: L1 cache and AXI DMA (Yosys/OpenSTA, Sky130); 428/431 tests pass on VerilogEval and CVDP.