A Survey of Task-Oriented Knowledge Graph Reasoning: Status, Applications, and Prospects

Time-R1: Framework and resources for endowing LLMs with comprehensive temporal reasoning (understanding, prediction, creative generation) using a novel three-stage RL curriculum. Includes the Time-Bench dataset and pre-trained models.

[WSDM 2022] EvoKG: Jointly Modeling Event Time and Network Structure for Reasoning over Temporal Knowledge Graphs

The official repo of TimeLlama, an instruction-finetuned Llama2 series that improve complex temporal reasoning ability.

Qualitative Reasoning: Spatio-Temporal Reasoning using Relation Algebras and Constraint Networks. Documentation is under construction at ReadTheDocs. See link below.

The repository for ACL 2024 paper "TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models"

[AAAI 2022] TempoQR: Temporal Question Reasoning over Knowledge Graphs

Code and data for "Living in the Moment: Can Large Language Models Grasp Co-Temporal Reasoning?" (ACL 2024)

TRAM: Benchmarking Temporal Reasoning for Large Language Models (Findings of ACL 2024)

Code and data for "Timo: Towards Better Temporal Reasoning for Language Models" (COLM 2024)

A semantic cognitive engine for software systems. By combining MCP context with iterative "dream cycles," DreamGraph builds an abstract representation of system intent—acting as a universal architect capable of autonomous reasoning, system abstraction, and cross-ecosystem transcompilation.

[EMNLP 2023] Question Answering as Programming for Solving Time-Sensitive Questions

[ACL 2025] Learning to Reason Over Time: Timeline Self-Reflection for Improved Temporal Reasoning in Language Models

LfD framework for learning the temporal dynamics of a social interaction

Modular network control plane verification tool, using temporal invariants to define modular interfaces

Algebras for qualitative reasoning about time, space, etc.

🔥 Measuring Massive Multimodal Understanding and Reasoning in Open Space

🌐 Enhance embodied AI with continuous vision-language understanding for dynamic environment adaptation and achieve accurate multi-step temporal reasoning.

Persistent Anticipation Layer for LLM Agents — Pre-compute possible futures during idle time for faster, cheaper and more proactive decisions. Bio-inspired temporal awareness (short/medium/long-term horizons).

Temporal reasoning for AI agents — deadlines, schedules, sequences, duration estimation (PERT), and decay models. One .atime file. Rust core + MCP server.