Papers Arithmetic Reasoning
“Arithmetic Reasoning” 태그가 달린 논문 214편 · 필터 해제
Dual-Manifold Geometry Guided Representation Learning: Adaptive Coupling between Kernel and Data Spaces
Deep representation learning has primarily focused on how features evolve across network layers, while largely overlooking the structured geometry embedded in network parameters. We introduce a dual-manifold perspective …
Representation LearningArithmetic ReasoningImage ClassificationThink Short, Defer Smart, Act, and Repeat: Calibrated Reasoning and Uncertainty-Aware Deferral for Edge LLM Agents
LLM agents following the ReAct paradigm are promising enablers of complex multi-step tasks, including multi-hop question answering, code generation, and control of physical AI systems. Yet, when deployed at the edge, the…
Multi-hop Question AnsweringArithmetic ReasoningCode GenerationLogit-Contribution Scoring Identifies Non-Literal Retrieval Heads
In long-context use, large language models frequently synthesize answers from the meaning of a relevant context span rather than literally copy-pasting them. Identifying which attention heads perform this synthesis matte…
Arithmetic ReasoningNeuReasoner: Theory-grounded Mapping of Reasoning Elicitation Boundaries
A growing body of work suggests that the reasoning capabilities of large language models are largely latent in their base form, with post-training primarily amplifying rather than introducing them. However, this evidence…
Arithmetic ReasoningCode GenerationDecision MakingArithmetic Pedagogy for Language Models
We investigate whether methods of human mathematics pedagogy can guide the training of language models toward arithmetic reasoning. Building on the GASING method -- an Indonesian pedagogy that solves basic arithmetic thr…
Reinforcement LearningArithmetic ReasoningTesting LLM Arithmetic Reasoning Generalization with Automatic Numeric-Remapping Attacks
Large language models achieve strong performance on arithmetic reasoning benchmarks, and one common response to arithmetic brittleness is to delegate computation to code. Yet models are still often used in settings where…
Arithmetic ReasoningStrategic Over-Parameterization for Generalizable Low-Rank Adaptation
Adapting large language models (LLMs) to downstream tasks via full fine-tuning is increasingly impractical due to its computational and memory demands. Parameter-efficient fine-tuning (PEFT) approaches such as Low-Rank A…
parameter-efficient fine-tuningArithmetic ReasoningCode GenerationProbabilistic Calibration Is a Trainable Capability in Language Models
Language models are increasingly used in settings where outputs must satisfy user-specified randomness constraints, yet their generation probabilities are often poorly calibrated to those targets. We study whether this c…
Arithmetic ReasoningKnowledge Graphs, the Missing Link in Agentic AI-based Formal Verification
Recent advances in Large Language Models (LLMs) have enabled workflows that generate SystemVerilog Assertions (SVAs) from natural-language specifications, with the potential to accelerate Formal Verification (FV). Howeve…
Arithmetic ReasoningKnowledge GraphsWhat Do Claim Verification Datasets Actually Test? A Reasoning Trace Analysis
Despite rapid progress in claim verification, we lack a systematic understanding of what reasoning these benchmarks actually exercise. We generate structured reasoning traces for 24K claim-verification examples across 9 …
Arithmetic ReasoningPolaris: A Gödel Agent Framework for Small Language Models through Experience-Abstracted Policy Repair
Gödel agent realize recursive self-improvement: an agent inspects its own policy and traces and then modifies that policy in a tested loop. We introduce Polaris, Gödel agent for compact models that performs policy repair…
Arithmetic ReasoningThinking in Different Spaces: Domain-Specific Latent Geometry Survives Cross-Architecture Translation
We investigate whether independently trained language models converge to geometrically compatible latent representations, and whether this compatibility can be exploited to correct model behavior at inference time withou…
Mathematical ReasoningArithmetic ReasoningASDA: Automated Skill Distillation and Adaptation for Financial Reasoning
Adapting large language models (LLMs) to specialized financial reasoning typically requires expensive fine-tuning that produces model-locked expertise. Training-free alternatives have emerged, yet our experiments show th…
Arithmetic ReasoningDomain AdaptationLarge Language Models for Math Education in Low-Resource Languages: A Study in Sinhala and Tamil
Large language models (LLMs) have achieved strong results in mathematical reasoning, and are increasingly deployed as tutoring and learning support tools in educational settings. However, their reliability for students w…
Mathematical ReasoningArithmetic Reasoning$\mathcal{X}$-KD: General Experiential Knowledge Distillation for Large Language Models
Knowledge Distillation (KD) for Large Language Models (LLMs) has become increasingly important as models grow in size and complexity. While existing distillation approaches focus on imitating teacher behavior, they often…
Knowledge DistillationReinforcement LearningArithmetic ReasoningMachine TranslationGeneration Order and Parallel Decoding in Masked Diffusion Models: An Information-Theoretic Perspective
Masked Diffusion Models (MDMs) significantly accelerate inference by trading off sequential determinism. However, the theoretical mechanisms governing generation order and the risks inherent in parallelization remain und…
Arithmetic ReasoningAdaFuse: Adaptive Ensemble Decoding with Test-Time Scaling for LLMs
Large language models (LLMs) exhibit complementary strengths arising from differences in pretraining data, model architectures, and decoding behaviors. Inference-time ensembling provides a practical way to combine these …
Open-Domain Question AnsweringArithmetic ReasoningMachine TranslationThe Two-Stage Decision-Sampling Hypothesis: Understanding the Emergence of Self-Reflection in RL-Trained LLMs
Self-reflection capabilities emerge in Large Language Models after RL post-training, with multi-turn RL achieving substantial gains over SFT counterparts. Yet the mechanism of how a unified optimization objective gives r…
Arithmetic ReasoningReliability Under Randomness: An Empirical Analysis of Sparse and Dense Language Models Across Decoding Temperatures
The increasing prevalence of sparse Mixture-of-Experts (MoE) architectures in large language models raises important questions regarding their reliability under stochastic decoding. While conditional computation enables …
Computational EfficiencyArithmetic ReasoningLLMBoost: Make Large Language Models Stronger with Boosting
Ensemble learning of LLMs has emerged as a promising alternative to enhance performance, but existing approaches typically treat models as black boxes, combining the inputs or final outputs while overlooking the rich int…
Arithmetic ReasoningEnsemble Learning