In real-world Tool-Integrated Reasoning (TIR) scenarios, where LLMs interleave reasoning with external tool calls, a major source of inefficiency is that the toolcalls create pauses between LLM requests and cause KV-Cache eviction, forcing recomputation.Also, the long, unfiltered response returned by external tools inflates the KV-Cache, so each decode step spends more time loading the growing cache and thus becomes steadily slower as context length increases.However, existing efficiency metrics like token counts and toolcall counts fail to capture the real model inference latency.To address this, we introduce PTE (Prefill Token Equivalents), a hardwareaware TIR-efficiency metric that unifies internal reasoning and external tool-use costs while explicitly accounting for non-reusable KV-Cache and long-tool-response scenarios.Validation in a high-concurrency industrial setting indicates that PTE aligns significantly better with wall-clock latency than standard token counts, while maintaining consistent efficiency rankings across diverse hardware profiles.We conduct extensive experiments across five TIR benchmarks, quantify their PTE costs, and identify four inefficiency patterns that appear in TIR.We also discover that trajectories with higher PTE costs tend to have lower reasoning correctness, indicating that simply using more tools does not improve the quality of the answer.The code is available at https://github.com/sqs-ustc/ tool-reasoning-framework-PTE.

Pretrained language models (LMs) are prone to arithmetic errors.Existing work showed limited success in probing numeric values from models' representations, indicating that these errors can be attributed to the inherent unreliability of distributionally learned embeddings in representing exact quantities.However, we observe that previous probing methods are inadequate for the emergent structure of learned number embeddings with sinusoidal patterns.In response, we propose a novel probing technique that decodes numeric values from input embeddings with near-perfect accuracy across a range of open-source LMs.This proves that after the sole pre-training, LMs represent numbers with remarkable precision.Finally, we find that the embeddings' precision, judged by our probe's accuracy, explains a large portion of LM's errors in elementary arithmetic, and show that aligning the embeddings with the pattern our probes discover can mitigate these errors.

High-quality image captions are essential for improving modality alignment and visual understanding in Large Vision-Language Models (LVLMs).However, the scarcity of ultradetailed image caption data limits further advancements.This paper presents a systematic pipeline for generating high-quality, ultradetailed image captions, encompassing both pre-processing and post-processing stages.In the pre-processing stage, we classify and deduplicate images, extract visual information using expert tools, and leverage GPT-4o with structured prompts to generate initial captions.To enhance comprehensiveness, we introduce an expansion strategy based on Large Language Models (LLMs), defining eight descriptive dimensions to refine and extend captions, which serve as seed data for training a proprietary captioner model.In the post-processing stage, we incorporate human error-correction annotations and an active learning-inspired approach to refine low-quality samples.Using high-quality corrected data, we apply Direct Preference Optimization (DPO) and develop a critic-rewrite pipeline, training a sentence-level critic model to mitigate hallucinations.Experimental results demonstrate that our ultra-detailed captions significantly enhance LVLMs' perception and cognitive abilities across multiple vision-language benchmarks.The code and dataset are available at https://github.com/yuzeng0-0/UltraCaption.