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Found 20 papers, 6 papers with code
CoThink: Token-Efficient Reasoning via Instruct Models Guiding Reasoning Models
With reference to the instruct model, we formally define reasoning efficiency and observe a potential reasoning efficiency scaling law in LLMs.
If an LLM Were a Character, Would It Know Its Own Story? Evaluating Lifelong Learning in LLMs
However, during multi-turn, multi-agent interactions, LLMs begin to exhibit consistent, character-like behaviors, hinting at a form of emergent lifelong learning.
Position-Aware Depth Decay Decoding ($D^3$): Boosting Large Language Model Inference Efficiency
Experiments on large language models (\ie the Llama) with $7 \sim 70$ billion parameters show that $D^3$ can achieve an average 1. 5x speedup compared with the full-inference pipeline while maintaining comparable performance with nearly no performance drop ($<1\%$) on the GSM8K and BBH benchmarks.
Grid and Road Expressions Are Complementary for Trajectory Representation Learning
Trajectory representation learning (TRL) maps trajectories to vectors that can be used for many downstream tasks.
TEAM: Topological Evolution-aware Framework for Traffic Forecasting--Extended Version
However, existing methods assume the input is fixed-topology road networks and static traffic time series.
Sketch: A Toolkit for Streamlining LLM Operations
Sketch comprises the following components: (1) a suite of task description schemas and prompt templates encompassing various NLP tasks; (2) a user-friendly, interactive process for building structured output LLM services tailored to various NLP tasks; (3) an open-source dataset for output format control, along with tools for dataset construction; and (4) an open-source model based on LLaMA3-8B-Instruct that adeptly comprehends and adheres to output formatting instructions.
Open-domain Implicit Format Control for Large Language Model Generation
Controlling the format of outputs generated by large language models (LLMs) is a critical functionality in various applications.
PORCA: Root Cause Analysis with Partially Observed Data
In this paper, we unveil the issues of unobserved confounders and heterogeneity in partial observation and come up with a new problem of root cause analysis with partially observed data.
What Affects the Stability of Tool Learning? An Empirical Study on the Robustness of Tool Learning Frameworks
Tool learning methods have enhanced the ability of large language models (LLMs) to interact with real-world applications.
DRE: Generating Recommendation Explanations by Aligning Large Language Models at Data-level
Recommendation systems play a crucial role in various domains, suggesting items based on user behavior. However, the lack of transparency in presenting recommendations can lead to user confusion.
Not All Layers of LLMs Are Necessary During Inference
That is, not all layers of LLMs are necessary during inference.
V2VSSC: A 3D Semantic Scene Completion Benchmark for Perception with Vehicle to Vehicle Communication
Extensive experiments demonstrate that by leveraging V2V communication, the SSC performance can be increased by 8. 3% on geometric metric IoU and 6. 0% mIOU.
Learning Time-aware Graph Structures for Spatially Correlated Time Series Forecasting
Spatio-temporal forecasting of future values of spatially correlated time series is important across many cyber-physical systems (CPS).
FLM-101B: An Open LLM and How to Train It with $100K Budget
Large language models (LLMs) are considered important approaches towards foundational machine intelligence, achieving remarkable success in Natural Language Processing and multimodal tasks, among others.
nanoLM: an Affordable LLM Pre-training Benchmark via Accurate Loss Prediction across Scales
With around 14% of the one-time pre-training cost, we can accurately forecast the loss for models up to 52B.
RetroGraph: Retrosynthetic Planning with Graph Search
We observe that the same intermediate molecules are visited many times in the searching process, and they are usually independently treated in previous tree-based methods (e. g., AND-OR tree search, Monte Carlo tree search).
Ranked #2 on
Multi-step retrosynthesis
on USPTO-190
GEO satellites on-orbit repairing mission planning with mission deadline constraint using a large neighborhood search-genetic algorithm
This paper proposed a novel large neighborhood search-adaptive genetic algorithm (LNS-AGA) for many-to-many on-orbit repairing mission planning of geosynchronous orbit (GEO) satellites with mission deadline constraint.
PT-ResNet: Perspective Transformation-Based Residual Network for Semantic Road Image Segmentation
This linear model is then utilized to reduce the redundant information in the left and right road images.
Fast Constraint Propagation for Image Segmentation
The experimental results demonstrate the promising performance of the proposed method for segmentation with selectively propagated constraints.
