SeCo performs semantic-driven context compression for LLMs by anchoring on query-relevant semantic centers and applying consistency-weighted token merging, yielding better downstream performance, lower latency, and stronger out-of-domain robustness than position-based methods across 14 benchmarks.
SCOPE: A generative approach for LLM prompt compression.CoRR, abs/2508.15813
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LLM compression of filings and earnings calls often changes the source-implied bear/neutral/bull decision; agentic multi-candidate auditing against the source reduces those flips.
CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.
citing papers explorer
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Beyond Position Bias: Shifting Context Compression from Position-Driven to Semantic-Driven
SeCo performs semantic-driven context compression for LLMs by anchoring on query-relevant semantic centers and applying consistency-weighted token merging, yielding better downstream performance, lower latency, and stronger out-of-domain robustness than position-based methods across 14 benchmarks.
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When Summaries Distort Decisions: Information Fidelity in LLM-Compressed Financial Analysis
LLM compression of filings and earnings calls often changes the source-implied bear/neutral/bull decision; agentic multi-candidate auditing against the source reduces those flips.
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CRAFT: Cost-aware Refinement And Front-aware Tuning of Prompts
CRAFT is a Pareto-front prompt optimizer that allocates scarce LLM validation calls to candidates near the current front using accuracy- and cost-oriented generators plus NSGA-II retention.