One Size Fits None: Heuristic Collapse in LLM Investment Advice

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ross, Jillian, Lo, Andrew W.
Format:	Preprint
Published:	2026
Subjects:	Computation and Language Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

One Size Fits None: Rethinking Fairness in Medical AI
by: Roller, Roland, et al.
Published: (2025)

One Size Fits None: Modeling NYC Taxi Trips
by: Eglinskas, Tomas
Published: (2025)

No One Size Fits All: QueryBandits for Hallucination Mitigation
by: Cho, Nicole, et al.
Published: (2026)

LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory
by: Ross, Jillian, et al.
Published: (2024)

One Size Does Not Fit All: Token-Wise Adaptive Compression for KV Cache
by: Lu, Liming, et al.
Published: (2026)

One Size Does Not Fit All: A Distribution-Aware Sparsification for More Precise Model Merging
by: Luo, Yingfeng, et al.
Published: (2025)

All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling
by: Marconato, Emanuele, et al.
Published: (2024)

No One-Size-Fits-All: Building Systems For Translation to Bashkir, Kazakh, Kyrgyz, Tatar and Chuvash Using Synthetic And Original Data
by: Karpov, Dmitry
Published: (2026)

Frequency Explains the Inverse Correlation of Large Language Models' Size, Training Data Amount, and Surprisal's Fit to Reading Times
by: Oh, Byung-Doh, et al.
Published: (2024)

Estimating Semantic Alphabet Size for LLM Uncertainty Quantification
by: McCabe, Lucas H., et al.
Published: (2025)

BERTer: The Efficient One
by: Saligram, Pradyumna, et al.
Published: (2024)

Scaling with Collapse: Efficient and Predictable Training of LLM Families
by: Bergsma, Shane, et al.
Published: (2025)

HeuriGym: An Agentic Benchmark for LLM-Crafted Heuristics in Combinatorial Optimization
by: Chen, Hongzheng, et al.
Published: (2025)

One Token to Fool LLM-as-a-Judge
by: Zhao, Yulai, et al.
Published: (2025)

One Jump Is All You Need: Short-Cutting Transformers for Early Exit Prediction with One Jump to Fit All Exit Levels
by: Seshadri, Amrit Diggavi
Published: (2025)

The Heuristic Core: Understanding Subnetwork Generalization in Pretrained Language Models
by: Bhaskar, Adithya, et al.
Published: (2024)

Collapse of Irrelevant Representations (CIR) Ensures Robust and Non-Disruptive LLM Unlearning
by: Sondej, Filip, et al.
Published: (2025)

AQUA-Bench: Beyond Finding Answers to Knowing When There Are None in Audio Question Answering
by: Kuan, Chun-Yi, et al.
Published: (2026)

The Collapse of Heterogeneity in Silicon Philosophers
by: Shi, Yuanming, et al.
Published: (2026)

From Signal Degradation to Computation Collapse: Uncovering the Two Failure Modes of LLM Quantization
by: Zhou, Chenxi, et al.
Published: (2026)

Paloma: A Benchmark for Evaluating Language Model Fit
by: Magnusson, Ian, et al.
Published: (2023)

Mistletoe: Stealthy Acceleration-Collapse Attacks on Speculative Decoding
by: Sun, Shuoyang, et al.
Published: (2026)

Representation Collapse in Machine Translation Through the Lens of Angular Dispersion
by: Tokarchuk, Evgeniia, et al.
Published: (2026)

Seq-VCR: Preventing Collapse in Intermediate Transformer Representations for Enhanced Reasoning
by: Arefin, Md Rifat, et al.
Published: (2024)

SepLLM: Accelerate Large Language Models by Compressing One Segment into One Separator
by: Chen, Guoxuan, et al.
Published: (2024)

The Effect of Model Size on LLM Post-hoc Explainability via LIME
by: Heyen, Henning, et al.
Published: (2024)

TreePO: Bridging the Gap of Policy Optimization and Efficacy and Inference Efficiency with Heuristic Tree-based Modeling
by: Li, Yizhi, et al.
Published: (2025)

Dimensional Collapse in Transformer Attention Outputs: A Challenge for Sparse Dictionary Learning
by: Wang, Junxuan, et al.
Published: (2025)

Cram Less to Fit More: Training Data Pruning Improves Memorization of Facts
by: Ye, Jiayuan, et al.
Published: (2026)

Problems with Chinchilla Approach 2: Systematic Biases in IsoFLOP Parabola Fits
by: Czech, Eric, et al.
Published: (2026)

I Can't Believe It's Not Robust: Catastrophic Collapse of Safety Classifiers under Embedding Drift
by: Sahoo, Subramanyam, et al.
Published: (2026)

When Sharpening Becomes Collapse: Sampling Bias and Semantic Coupling in RL with Verifiable Rewards
by: Fan, Mingyuan, et al.
Published: (2026)

Breaking Quadratic Barriers: A Non-Attention LLM for Ultra-Long Context Horizons
by: Kiruluta, Andrew, et al.
Published: (2025)

FitCF: A Framework for Automatic Feature Importance-guided Counterfactual Example Generation
by: Wang, Qianli, et al.
Published: (2025)

Two is better than one: A Collapse-free Multi-Reward RLIF Training Framework
by: Joarder, Shourov, et al.
Published: (2026)

The Price of Format: Diversity Collapse in LLMs
by: Yun, Longfei, et al.
Published: (2025)

Power Lines: Scaling Laws for Weight Decay and Batch Size in LLM Pre-training
by: Bergsma, Shane, et al.
Published: (2025)

Why LLM Safety Guardrails Collapse After Fine-tuning: A Similarity Analysis Between Alignment and Fine-tuning Datasets
by: Hsiung, Lei, et al.
Published: (2025)

BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models
by: Ben-Zaken, Elad, et al.
Published: (2021)

LLM Pretraining with Continuous Concepts
by: Tack, Jihoon, et al.
Published: (2025)