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New papers
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AI for Optimization
1 new papers | 1 | 110 total analyzed
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This paper replaces generic solver error messages with minimal unsatisfiable cores (MUCs) to guide LLM self-repair when translating natural language constraint problems into Answer Set Programming. The results are backed by a 77-instance benchmark, showing that MUC feedback reduces fabricated solutions on infeasible problems from 79% to 7% for weaker models, though strong models perform well even with generic chain-of-thought. The key insight is that raw solver errors (e.g., 'unsatisfiable') often cause LLMs to blindly delete valid constraints until a problem becomes solvable, whereas structural proof artifacts localize the conflict and allow the LLM to correctly identify genuine infeasibility. This is highly relevant for symbolic OR modeling and LLM-in-the-loop optimization; the community could adapt this by using Irreducible Infeasible Subsystems (IIS) from MIP solvers as a high-quality, leakage-free feedback signal for LLM evolutionary search or autoformalization pipelines.
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Automated Algorithm Discovery
5 new papers | 5 | 193 total analyzed
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Borthwick introduces a cost-targeted evolutionary meta-agent that writes Python programs utilizing a menu of differently-priced LLMs to optimize the cost-accuracy Pareto frontier. The results are highly rigorous, Pareto-dominating nearly all baselines (including hand-engineered systems) on two AstaBench tasks using only 66 to 100 training examples. The key insight is that exposing a priced menu of LLMs to the evolutionary search and using a graded cost-penalty objective (pricing cost overages in error-equivalents) naturally forces the discovery of cheap-first cascades and selective strong-model escalation.
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ε-MemEvo introduces a cross-task memory framework for LLM program evolution that stores natural-language strategy summaries from prior tasks and uses a contextual Thompson Sampling gate to control their injection into new tasks. The results are rigorously backed by empirical data, demonstrating an 8.7% improvement in convergence area (AUCC) and a 9.4% boost in early-stage convergence across 8 optimization benchmarks using GPT-5. The key insight is that cross-task memory transfer in evolutionary coding is fundamentally a safety problem; naive memory injection causes catastrophic negative transfer, but an adaptive bandit gate successfully suppresses mismatched tactics while retaining the benefits of aligned ones.
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MEGA is a three-layer infrastructure for self-evolving agent optimization that distills execution traces into a structured 'Wisdom Graph' and uses a multi-agent loop to iteratively improve agent workflows. The results are backed by strong empirical numbers, achieving a 76.55 aggregate score on four benchmarks (HotpotQA, IFBench, HoVer, PUPA) and outperforming SOTA optimizers like GEPA and TextGrad. The key insight is the 'Seed-Epoch' evaluation regime—which fixes the evaluation seed during an optimization epoch to eliminate data variance and isolate the true impact of strategy changes—alongside the decomposition of memory into atomic Primary-Context-Resultant (PCR) triplets for logical reasoning.
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2026-08-17 | Google DeepMind, MIT, Carnegie Mellon University, Columbia University | 2608.16884
This paper applies gradient-based optimization and AlphaEvolve to the combination loss analysis problem, improving the theoretical upper bound on the matrix multiplication exponent to ω< 2.371177. The results are rigorously verified using rational arithmetic, proving a genuine state-of-the-art advance over the previous 2.371339 bound. The key insight for LLM evolutionary search is the preparation pipeline: the authors relaxed discrete max-entropy constraints into a differentiable tensor program using the Sinkhorn-Knopp algorithm and Jax, enabling hardware parallelization up to 7 million parameters. They then applied AlphaEvolve using 'evolving constructions' to iteratively refine the optimizer while passing the best solution state between generations. This is highly relevant for our work, as it demonstrates how to successfully scale LLM-driven algorithm discovery for massive, highly constrained combinatorial problems.
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GoalEvolve is an LLM-based evolutionary search framework that optimizes physical design algorithms by targeting final full-flow metrics rather than stage-local objectives. The results are backed by strong empirical evidence, demonstrating a 30.67% improvement in total negative slack over default OpenROAD and outperforming standard Codex goal mode under matched evaluation budgets. The key insight is the use of 'effect-debt analysis' combined with an Evolutionary Program Database (EPD), which tracks whether a local algorithmic change causes downstream degradation and stores this as persistent mechanism memory to guide future LLM prompts.
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Optimizing AI Systems
1 new papers | 0 | 166 total analyzed
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2026-08-12 | The Hong Kong University of Science and Technology (Guangzhou), IDEA Research, DataArcTech Ltd. | 2608.11919
LazyTrain formulates limited-resource LLM training, specifically activation offloading, recomputation and communication overlap, as a mixed-integer linear programming (MILP) scheduling problem. The results are backed by concrete hardware measurements, demonstrating a 1.24x TFLOPS improvement over heuristic baselines on a single H800 for a 27B model. The key insight is that treating heterogeneous memory offloading as a joint MILP path-selection problem, rather than a greedy heuristic, enables the solver to perfectly hide PCIe and NVMe transfer costs within compute windows.
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