NEWS

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mLLMCelltype 2.0.5 (2026-05-11)

Bug Fixes

• Unified discussion cell-type extraction across consensus checking and early discussion fallback paths.
• Scoped Qwen endpoint detection cache by API key to avoid cross-credential endpoint reuse.
• Forwarded registered custom model configuration to custom providers that accept model_config or ....

Documentation

• Updated caching guidance, model examples, and Python integration examples for current package behavior.

mLLMCelltype 2.0.4

Bug Fixes

• Aligned R documentation with the actual annotate_cell_types() return contract.
• Hardened discussion error-response filtering so failed model responses are excluded consistently.
• Kept default tests offline by gating real API integration tests behind explicit environment variables.

Documentation

• Updated examples and integration guidance for the current cross-language API surface.

mLLMCelltype 2.0.0 (2026-02-08)

Breaking Changes

• Version bump to 2.0.0: Major release reflecting substantial architectural improvements since 1.2.9
• Logger defaults changed: UnifiedLogger console output is now disabled by default (enable_console = FALSE). Use configure_logger(console_output = TRUE) to enable console logging.

New Model Support

• Added support for latest LLM models across all providers:
  • OpenAI: GPT-5.2, GPT-5.1, GPT-5, o3-pro, o4-mini
  • Anthropic: Claude Opus 4.6, Claude Opus 4.5, Claude Sonnet 4.5
  • Google: Gemini 3 Pro, Gemini 3 Flash, Gemini 2.5 Pro/Flash
  • X.AI: Grok 4, Grok 4.1, Grok 4 Heavy
  • DeepSeek: DeepSeek R1
  • Alibaba: Qwen 3 Max
  • Zhipu: GLM-4.7
  • MiniMax: MiniMax M2.1

Architecture Improvements

• Unified logging system: Consolidated dual debug mechanisms (debug parameter + LLMCELLTYPE_DEBUG_CACHE env var) into single log_debug() approach; debug = TRUE temporarily enables console + DEBUG level via on.exit()
• Complete audit trail: All warning() calls now paired with log_warn() for file-based audit; LLM consensus decisions logged to audit trail
• NAMESPACE fix: QwenProcessor now correctly exported; internal .qwen_endpoint_cache no longer exposed in public API
• Cluster ID preservation: Cluster IDs from input are preserved as-is throughout the annotation pipeline

Bug Fixes

• Fixed @export misplacement in qwen_processor.R that exported internal state instead of the processor class
• Fixed missing tissue_name parameter in real API test calls
• Fixed print() bypassing logging system in compare_model_predictions.R
• Fixed trailing newlines causing double-spaced console output in compare_model_predictions.R
• Fixed message() format inconsistencies across consensus and comparison functions
• Improved NA handling in API key validation and print functions

Code Quality

• Removed marketing language from documentation
• Consistent message() + log_info() pairing across entire codebase
• Removed dead TaskState.TESTING from state machine (web component)

mLLMCelltype 1.3.6

Documentation Clarification

• Clarified current cluster ID contract in tutorials and examples: input cluster IDs are preserved as-is
• Updated mapping examples to use annotation names directly (instead of assuming sequential 0:(n-1) IDs)
• Kept historical release notes unchanged; this entry documents the current behavior explicitly

mLLMCelltype 1.3.5

Updated Models

• Updated Anthropic Claude model support to include latest releases:
  • Added Claude Sonnet 4.5 (claude-sonnet-4-5-20250929) - Newest Sonnet model, same price as earlier Sonnets
  • Added Claude Opus 4.1 (claude-opus-4-1-20250805) - Updated Opus model
  • Added Claude 4 series models (claude-sonnet-4-20250514, claude-opus-4-20250514)
  • Added support for both date-versioned and alias formats (e.g., claude-sonnet-4.5, claude-opus-4.1)
• Updated all documentation, vignettes, and examples to recommend latest models
• All Sonnet models (4.5, 4, 3.5, 3.7) have identical pricing - Claude Sonnet 4.5 is now supported at identical pricing

Documentation Updates

• Updated model recommendations in usage tutorials and vignettes
• Updated get_provider() documentation with comprehensive model lists
• Added clarification that dated model versions are identical across all platforms

Notes

• OpenRouter now supports anthropic/claude-sonnet-4.5 and anthropic/claude-opus-4.1
• All dated model versions (e.g., 20250929) remain stable and do not change over time

mLLMCelltype 1.3.4

Bug Fixes

• Fixed critical NA handling issue in base_api_processor.R that caused "missing value where TRUE/FALSE needed" errors
• Fixed similar NA handling issues in print_consensus_summary.R
• Improved robustness of API key validation to handle NA, NULL, and empty string cases correctly

Improvements

• Enhanced error handling for invalid API keys
• Better support for OpenRouter API integration
• Improved stability when processing consensus annotations

mLLMCelltype 1.3.2 (2025-09-02)

Official CRAN Release

• mLLMCelltype is now available on CRAN: install.packages("mLLMCelltype")
• CRAN page: https://CRAN.R-project.org/package=mLLMCelltype
• DOI: 10.32614/CRAN.package.mLLMCelltype

Installation

• Standard R package installation from CRAN repository
• Package has passed CRAN checks for reliability and compatibility
• Tested across R versions and platforms (Windows, macOS, Linux)

Features

• Automated cell type annotation using consensus from multiple LLMs
• Support for OpenAI, Anthropic, Google, and other providers through OpenRouter
• Integration with Seurat workflows
• Uncertainty quantification for annotations
• Caching system for API cost reduction
• Interactive consensus building with multi-model discussion

mLLMCelltype 1.3.1

New Features: Enhanced Cluster Analysis Control

Selective Cluster Analysis

• New parameter clusters_to_analyze: Allows users to specify exactly which clusters to analyze
  • No need to manually filter input data
  • Maintains original cluster numbering
  • Reduces API calls and costs by focusing on relevant clusters
  • Perfect for iterative refinement and subtyping workflows

Cache Control for Re-analysis

• New parameter force_rerun: Forces fresh analysis of controversial clusters
  • Bypasses cache when you need new analysis with different context
  • Essential for subtype identification with tissue-specific context
  • Non-controversial clusters still benefit from cache performance
  • Combines perfectly with clusters_to_analyze for targeted workflows

Use Cases

• Iterative Subtyping: Analyze broad cell types first, then focus on specific populations
• Cost-Effective Re-analysis: Only re-analyze controversial or specific clusters
• Context-Specific Analysis: Re-run with different tissue contexts without cache interference
• Targeted Refinement: Focus computational resources on clusters of interest

Implementation

• Full backward compatibility maintained
• Comprehensive input validation and error handling
• Extensive testing with real API keys across multiple scenarios
• Updated documentation with detailed examples

mLLMCelltype 1.2.9

Consensus check optimization

Performance Optimization

• Two-Stage Consensus Strategy: Implemented optimized consensus checking that reduces redundant API calls through early consensus detection
  • Stage 1 - Simple Consensus: First performs fast local calculation based on normalized annotations
  • Stage 2 - LLM Verification: Only calls LLM for clusters that don't meet consensus thresholds
  • Smart Resource Usage: LLM only used for genuinely ambiguous cases

Cost Reduction

• API cost optimization:
  • Clear consensus cases (majority of clusters) now processed without LLM calls
  • Reduces costs proportionally to API call reduction
  • Maintains same accuracy while being much more economical

Implementation Details

• New Functions:
  • normalize_annotation(): Handles annotation variations (e.g., "T cells" vs "T lymphocytes")
  • calculate_simple_consensus(): Fast local consensus calculation with CP and entropy metrics
• Improved Fallback: If LLM fails, falls back to simple consensus results instead of default values
• Enhanced Logging: Clear indication of which strategy was used for each cluster

mLLMCelltype 1.2.7

Major Enhancements: Code Deduplication and Enhanced Logging

Code Architecture Refactoring

• BaseAPIProcessor: Introduced abstract base class for API processing with unified error handling and logging
  • Reduced code duplication across API processors
  • Standardized interface: All processors now inherit from BaseAPIProcessor with consistent make_api_call() and extract_response_content() methods
  • Unified error handling: Centralized exception handling and retry logic across all API providers

Enhanced Discussion Logging

• Fixed Discussion Format Issues: Resolved fragmented discussion logs where multi-line model responses were split into multiple entries
  • Improved readability: Multi-line model predictions now properly consolidated into single coherent text blocks
  • Better structure: Clean Markdown format with proper headers and code block formatting
  • Complete conversations: Full model reasoning (grounds, warrant, backing, qualifier, rebuttal) preserved in readable format

Complete API Request/Response Logging

• New Feature: Full API audit trail with complete request and response content
  • Dual format logging: Both JSON (machine-readable) and Markdown (human-readable) formats
  • Complete transparency: Every API call now logged with full prompt content and response
  • Debugging support: Detailed metadata including chunk information, timing, and response characteristics
  • Session organization: API logs organized in dedicated api_logs/ subdirectories within each session

Improved Log Organization

• Enhanced directory structure:

  logs/
  ├── mllm_YYYYMMDD_HHMMSS.log          # Main session log
  ├── YYYYMMDD_HHMMSS/                   # Session directory
  │   ├── cluster_X_discussion.md        # Discussion logs (fixed format)
  │   └── api_logs/                      # Complete API call logs (NEW)
  │       ├── provider_model_timestamp.json
  │       └── provider_model_timestamp.md
  

Performance Improvements

• Reduced codebase size: Consolidated API processor code
• Maintainability: Single source of truth for API processing logic
• Consistency: Standardized error messages and logging across all providers

Quality Assurance

• Comprehensive testing: All changes verified with real API calls using OpenRouter
• Backward compatibility: Existing functionality preserved while enhancing logging capabilities
• Zero breaking changes: All public APIs remain unchanged

mLLMCelltype 1.2.6

Major Logging System Overhaul

• Unified Logging System: Replaced fragmented logging infrastructure with comprehensive unified logging
  • Removed: Old DiscussionLogger class and scattered write_log calls
  • Added: New UnifiedLogger R6 class with structured JSON output, multi-level logging, and performance monitoring
  • New functions: configure_logger(), get_logger(), log_info(), log_warn(), log_error(), log_debug()
  • Features: Session tracking, API call monitoring, cache operation logging, automatic log rotation

Performance Improvements

• Performance Monitoring: Integrated performance tracking with session duration, API call counting, and error statistics
• Log Rotation: Automatic log file rotation with configurable size limits and file retention
• Structured Logging: JSON-formatted logs with context metadata for better analysis and debugging

API Enhancements

• Function Signature Updates: Removed logger parameters from all consensus annotation functions
• Code Cleanup: Eliminated 20+ scattered logger parameter dependencies across the codebase
• Cache Integration: Enhanced cache operations with detailed logging and performance tracking

Documentation Updates

• Updated Vignettes: Revised advanced features tutorial with new unified logging examples
• Roxygen Documentation: Updated all function documentation to reflect new logging system
• NAMESPACE Updates: Added exports for new logging functions and UnifiedLogger class

Breaking Changes

• Logger Parameters: Removed logger parameters from interactive_consensus_annotation() and related functions
• DiscussionLogger: Deprecated DiscussionLogger class (still exported for backward compatibility but not recommended)

mLLMCelltype 1.2.5

Model Updates

• Updated Gemini model support: Added support for new Gemini models and removed discontinued ones
  • Added gemini-2.0-flash-lite to supported models list
  • Updated documentation to reflect Google's model migration recommendations
  • Removed references to discontinued Gemini 1.5 Pro 001 and Gemini 1.5 Flash 001 models

Important Notes

• Google Gemini Model Migration: Google has discontinued several Gemini 1.5 models:
  • Already discontinued: Gemini 1.5 Pro 001, Gemini 1.5 Flash 001
  • Will be discontinued on Sept 24, 2025: Gemini 1.5 Pro 002, Gemini 1.5 Flash 002, Gemini 1.5 Flash-8B -001
  • Recommended migration: Use gemini-2.0-flash or gemini-2.0-flash-lite for better performance and continued support
  • The aliases gemini-1.5-pro and gemini-1.5-flash will continue to work until September 24, 2025

Documentation Updates

• Updated all README files (English and international versions) with new Gemini model information
• Updated R documentation and vignettes to reflect model changes
• Added migration guidance in main documentation

mLLMCelltype 1.2.4

Critical Bug Fixes

• Fixed major as.logical(from) error: Resolved critical error that occurred when processing large numbers of clusters (60+ clusters), which was caused by non-character data being passed to strsplit() functions
• Enhanced error handling for API responses: Added comprehensive tryCatch() blocks around all strsplit() operations in API processing functions
• Improved response validation: Added robust type checking for API responses to prevent function/closure types from being processed as character strings

Improvements

• Enhanced API processing robustness: All API processing functions (process_openrouter.R, process_anthropic.R, process_openai.R, process_deepseek.R, process_qwen.R, process_stepfun.R, process_minimax.R, process_zhipu.R, process_gemini.R, process_grok.R) now include improved error handling
• Better NULL value handling: Improved unlist() operations to filter out NULL values and handle errors gracefully
• Enhanced logging: Added more detailed error logging for debugging API response issues
• Improved consensus checking: Enhanced check_consensus.R to handle edge cases with malformed responses

Technical Details

• Fixed issue where large cluster datasets could cause type coercion errors in response parsing
• Added validation for function/closure types in API responses to prevent downstream errors
• Improved error messages to provide better diagnostics for API response issues

mLLMCelltype 1.2.3

Bug Fixes

• Fixed error handling in consensus checking when API responses are NULL or invalid
• Improved error logging for OpenRouter API error responses
• Added robust NULL and type checking in check_consensus function

Improvements

• Enhanced error diagnostics for OpenRouter API errors
• Added detailed logging of API error messages and response structures
• Improved robustness when handling unexpected API response formats

mLLMCelltype 1.2.2

Bug Fixes

• Fixed the 'non-character argument' error that occurred when processing API responses
• Added robust type checking for API responses across all model providers
• Improved error handling for unexpected API response formats

Improvements

• Added detailed error logging for API response issues
• Implemented consistent error handling patterns across all API processing functions
• Enhanced response validation to ensure proper structure before processing

mLLMCelltype 1.2.1

Improvements

• Added support for OpenRouter API
• Added support for free models through OpenRouter
• Updated documentation with examples for using OpenRouter models

mLLMCelltype 1.2.0

Features

• Added visualization functions for cell type annotation results
• Added support for uncertainty metrics visualization
• Implemented improved consensus building algorithm

mLLMCelltype 1.1.5

Bug Fixes

• Fixed an issue with cluster index validation that caused errors when processing certain CSV input files
• Improved error handling for negative indices with clearer error messages

Improvements

• Added example script for CSV-based annotation workflow (cat_heart_annotation.R)
• Enhanced input validation with more detailed diagnostics
• Updated documentation to clarify CSV input format requirements

mLLMCelltype 1.1.4

Bug Fixes

• Fixed a critical issue with cluster index handling, now the package strictly accepts only 0-based indices (compatible with Seurat)
• Fixed negative index (-1) issues that could occur when processing CSV input files
• Added strict validation for input cluster indices to ensure they start from 0

Improvements

• Removed automatic conversion logic from 1-based to 0-based indices in prompt_templates.R
• Added input validation in consensus_annotation.R to ensure cluster indices start from 0
• Updated code comments to clearly indicate that the package only accepts 0-based indices

mLLMCelltype 1.1.3

• Added support for X.AI's Grok models
• Updated the list of supported models, including Gemini 2.5 Pro
• Improved error handling and logging

mLLMCelltype 1.1.2

• Added support for Gemini 2.0 models
• Improved model response parsing
• Fixed cache management issues

mLLMCelltype 1.1.1

• Added support for Claude 3.7 and Claude 3.5 models
• Improved consensus building algorithm
• Fixed multiple minor bugs

mLLMCelltype 1.1.0

• Added interactive consensus annotation functionality
• Added multi-model discussion capability
• Improved cell type standardization

mLLMCelltype 1.0.0

• Initial release
• Support for cell type annotation using LLMs
• Support for models from OpenAI, Anthropic, and Google