arXiv subjects

Camila Ferreira

Publications and source records attributed to Camila Ferreira.

3 recordsLinked to original sources

arXiv2026

Structure-Token Evidence-Anchored Reasoning for Scientific Chart Understanding

Scientific charts encode quantities in axes, legends, and geometric marks, yet large vision-language models still treat them as natural photographs. Visual in-context examples do not expose the coordinate frame; unconstrained chain-of-thought can name a plausible number that was never read from a bar. We present STEER (Structure-Token Evidence-anchored Reasoning), which freezes a Llama-3.2-Vision encoder and inserts three modules: a chart structure graph encoder (CSGE) that binds ticks, legend items, and marks; evidence-anchored step reasoning (EASR) that forces every arithmetic step to cite a graph node; and weak-parser strong-reasoner alignment (WPSR) that uses a specialized table extractor only as a teacher of node attributes. On ChartQA, STEER reaches 82.70 average relaxed accuracy versus 80.16 for ChartGemma and 76.40 for a LLaVA-CoT backbone trained on the same mix. Gains widen on CharXiv reasoning (33.60 vs. 29.20 InternVL Chat V1.5) and ChartQAPro CoT (40.70 vs. 37.17 Qwen2-VL-7B), where OCR shortcuts disappear. Ablations show that dropping node serialization or numeric candidate constraints undoes most of the reasoning lift.

cs.CV↗

arXiv2026

SpaCE: Rethinking Spatial Capacity and Generalization in Multi-Frame Multimodal Large Language Models

Multi-modal large language models (MLLMs) have achieved remarkable empirical progress in spatial understanding through large-scale training on spatial visual question answering datasets. However, the theoretical foundations of multi-frame spatial reasoning remain entirely unexplored. We present SpaCE, a rigorous theoretical framework that characterizes the spatial reasoning capacity, sample complexity, and generalization guarantees of MLLMs operating on multi-frame inputs. We establish four main results. First, we prove an information-theoretic upper bound on spatial reasoning accuracy in terms of the mutual information between multi-frame observations and spatial targets. Second, we derive a sample complexity bound of order $Θ(d_{\mathrm{eff}} \cdot K_{\max} / (\varepsilon^2 \cdot δ))$, where $d_{\mathrm{eff}}$ is the effective spatial dimension and $K_{\max}$ bounds the KL divergence of the learned posterior. Third, we provide a PAC-Bayes generalization bound for multi-frame spatial reasoning under distribution shift. Fourth, we formally characterize the bias-variance trade-off between explicit 3D representations and implicit reasoning approaches, identifying the crossover conditions under which each paradigm is provably preferable. We validate our theoretical predictions on the MultiSPA, CA-VQA, and SpatialRGPT benchmarks, demonstrating that our bounds are empirically tight and that frame complementarity is the key driver of multi-frame spatial capacity. Our framework provides the first principled theoretical foundation for understanding when, why, and how multi-frame spatial reasoning in MLLs succeeds.

eess.IV↗

arXiv2026

Enhancing Self-Correction in Large Language Models through Multi-Perspective Reflection

While Chain-of-Thought (CoT) prompting advances LLM reasoning, challenges persist in consistency, accuracy, and self-correction, especially for complex or ethically sensitive tasks. Existing single-dimensional reflection methods offer insufficient improvements. We propose MyGO Poly-Reflective Chain-of-Thought (PR-CoT), a novel methodology employing structured multi-perspective reflection. After initial CoT, PR-CoT guides the LLM to self-assess its reasoning across multiple predefined angles: logical consistency, information completeness, biases/ethics, and alternative solutions. Implemented purely via prompt engineering, this process refines the initial CoT into a more robust and accurate final answer without model retraining. Experiments across arithmetic, commonsense, ethical decision-making, and logical puzzles, using GPT-three point five and GPT-four models, demonstrate PR-CoT's superior performance. It significantly outperforms traditional CoT and existing reflection methods in logical consistency and error correction, with notable gains in nuanced domains like ethical decision-making. Ablation studies, human evaluations, and qualitative analyses further validate the contribution of each reflection perspective and the overall efficacy of our poly-reflective paradigm in fostering more reliable LLM reasoning.

cs.CL↗